{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图像分类迁移学习\n",
    "\n",
    "在实际场景中，很少有人会选择从头训练整个网络，大多数情况下会基于已有的模型来进行迁移学习。本章将会以蚂蚁和蜜蜂的图像分类为例，讲解如何在MindSpore中加载预训练模型，并通过固定权重来实现迁移学习的目的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备环节\n",
    "\n",
    "首先，我们导入相关的模块，并配置运行信息。这里采用`GRAPH_MODE`运行，硬件环境为`GPU`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import stat\n",
    "import numpy as np\n",
    "\n",
    "import mindspore.ops as ops\n",
    "from mindspore.dataset.vision import Inter\n",
    "from mindspore.nn import Accuracy\n",
    "from mindspore.common.initializer import Normal\n",
    "import mindspore.nn as nn\n",
    "from mindspore import Model, Tensor, context\n",
    "import mindspore.dataset as ds\n",
    "import mindspore.dataset.vision.c_transforms as C\n",
    "import mindspore.dataset.transforms.c_transforms as C2\n",
    "from  mindspore import dtype as mstype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下载案例所用到的[蚂蚁与蜜蜂分类数据集](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/middleclass/hymenoptera_data.zip)，数据集中的图像来自于ImageNet，每个分类有大约120张训练图像与75张验证图像。将下载后的数据集放置在当前目录下：\n",
    "```\n",
    "project\n",
    "│  transfer_learning.ipynb\n",
    "│  resnet50.ckpt.ipynb     \n",
    "└─data\n",
    "   └─hymenoptera_data\n",
    "      └─train\n",
    "      │   └─ants\n",
    "      │   │  bees\n",
    "      └─val\n",
    "          └─ants\n",
    "          │  bees\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下载预训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下载[预训练模型的ckpt文件](https://download.mindspore.cn/model_zoo/official/cv/resnet/resnet50_v1.5_ascend_0.3.0_cifar10_official_classification_20200718/resnet50.ckpt)，将其放在上述目录。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据\n",
    "\n",
    "定义`create_dataset`函数来处理数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_path = 'data/hymenoptera_data/train'\n",
    "val_data_path = 'data/hymenoptera_data/val'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(data_path, batch_size=4, repeat_num=1,training=True):\n",
    "    \"\"\"定义数据集\"\"\"\n",
    "    data_set = ds.ImageFolderDataset(data_path, num_parallel_workers=8, shuffle=True)\n",
    "    image_size = 224\n",
    "    mean = [0.485*255 , 0.456*255 , 0.406*255]\n",
    "    std = [0.229*255, 0.224*255, 0.225*255]\n",
    "    if training:\n",
    "        trans = [\n",
    "                C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),\n",
    "                C.RandomHorizontalFlip(prob=0.5),\n",
    "                C.Normalize(mean=mean, std=std),\n",
    "                C.HWC2CHW()\n",
    "                ]\n",
    "    else:\n",
    "        trans = [\n",
    "                C.Decode(),\n",
    "                C.Resize(256),\n",
    "                C.CenterCrop(image_size),\n",
    "                C.HWC2CHW()\n",
    "                ]\n",
    "    type_cast_op = C2.TypeCast(mstype.int32)\n",
    "    # 实现dataset的映射和批量处理\n",
    "    data_set = data_set.map(operations=trans, input_columns=\"image\", num_parallel_workers=8)\n",
    "    data_set = data_set.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=8)\n",
    "    data_set = data_set.batch(batch_size, drop_remainder=True)\n",
    "    data_set = data_set.repeat(repeat_num)\n",
    "\n",
    "    return data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实例化数据集\n",
    "train_ds =  create_dataset(train_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化图像\n",
    "\n",
    "让我们可视化一些训练图像，来观察处理后的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor of image (4, 3, 224, 224)\n",
      "Labels: [1 0 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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wNirtqJfEc61q8UIJgw5ByYBiHJKWeLiu4SFjlvhKmSqMlSFfgEwBbBeqVagUIeaCcsSLrYe8bmbJ/OMaYBiQiotXnLNERa3tEDMhkoZ4L0RmQCIMcRWgSiGmfQUcZDiUgbz2IBMH6jWYmoD8GMQi0t5mEwpe25ri4lWHKg5W230kmh5m67YYw1veyp7fz+QDn57L3GXHwqueBaZLFRxbH7LsNUI2tfoU9foIOZ2huaqwdArsDE4+y/BYhWxF3HvbhJhZoaz3Y9cV8WgzynKQ3l6DoSys1bLGnA1ktXREA1Ya0OHCwm6kBMtoUX5mIF7LJJAGFYNeE9ocGVNrFPxwEcxbIMfVKrBvFL4wAt+rnIDhLSGDtIPnJlqXYlq3016rjfQI36A1ershJHDWDLEwWBWo171lfuOyViO9yV/zRRFd+V6QTQDS+oPdW4qWy5CJQs0ILpnRMOGCU4HKFAGm7HvVOdC+F11luo+45eC6bhHGszLImjdZzLriQn786O2EWjRnhTtp5noM/Wqc+l52b/wN+WIIlwQXX/aj50DRh0kUGWlF0SetSHQvTxCgPNypNME4HxZshfouCB/0vN12pM+WvN/5htZCngHyuZ4D5bvAqotneIixdQhcrkaZNuIxMOMcTerZPkLce+hKKMKhE0Dj+81QnGrCeuWXjqeh40pXE/S0wngnRGowVgDHEU83YkLMW42pFIRTEixLhEGHwQ6LWkZrEjzL5qGQg1IOlClNiLriAGgb6q70yZwDtg0tTd4jqophTlQhnQTdIgY4FofQDIj1CtSQUgYu7rTz34g0ZfGcBc+hcDWU6zCZhamqYMgJE4w4tMWhaHuLFxdqJXEmjBrUCnVcdSV/8ZHPMnJhiuUvPX6w55mXdvS9Ml8UjBaK7Bneju1OkXFqdDrQHt6NW3XYuUuzbhDsJLQUoD0ByRAMZKrEXLhiRT+JtIWigrYd6KuhBhCDu4BpI6SaYKYJp2Whsx1UKzDgipaiyCD3BwuipJkOnAV8PAHzL4yhlqfAgmizxdKJCf7PHVXW3g3bD+/kTycuct1OxGCWjn/400oTgTdbRQarbwD9paaFGOEY0AxmGtIRwZnKFTGEKA4NmPmvRmPsQwwhgqAdDb/1PEDT84zqGlIqcJi0K95I3YdDvJjNtIfuGVfDw5DdKoFHZ4hRtkvAQZgx0sbgnhDr7l9L5CxYtLOFKXMnRuTrWK3Xc//aYXY8tZ3/vrnCVOY5NrqqQacZr+0xxPDmEZimnaNiu3RD6GxQv0H2KZiL9IMQAoD3cCjE4BtUQHVJcHGaOVH1fudjuT6k5F+rEdM35gMzj2iK1prqwTtJGuOif9u7H7zfHja6dQGcdQrOeS/RuRc9E20dVVoM6EnDSDcUp0Dl5XoJA5pjkG6G9Axo6YZEDKLeywkJxDBWhf4qTOWhnIFaFio1MbqhKNTq4m3qGmSKMF4Rb5eUQA1tCZm4IhWBwUJeUDNkQrodQr3CKmgGUigPuWlUqgw7B/HZhoGsC4VRKAzL8zHCYHnMBBPx3OMRcG0oebADVTm2NNHBi178IXp6m5j1esXTlbM58Xq6GoYnq9y3e5TuEU05BC2T0DlmU67DQxvgge1gtENbj+A6KQMGhqEnBs2McNpMTUyBU4DowxAtA2dGocmGrD2NR0YisCwHRgcQL4PW4qU4iAEpMR2VT5jw5SgsaYPYDFAhG4ZzMK8V5i1AzVB0Zwa5bJ1m++QxW3dsKSLBl3kcO+L9TCVOACfYSHtjBMY37L1PyLHhTuhohiYtEeTxDGi/9/jLZgg8p0bvttEg+wY6SmB0PONtVyFfg1g0iAvFlESP7QhUXMFutW9Q/Gt7546EZZno+oCw/10OgYJ2QsqYzU03P0hhcpLiXfDX1+/k1WftRPckCHc9zCWXXMxDd+2mXD5hFP74ojydFhDaWC8BPavqfZ467HjvrzoHrA3gPAXmReLN0Y6E7TsIgnT+ZGYDEVDtUA2DVfPO1Th5+SwDOPI5uoDZwVGxLNcmfPAXEi/xn68/gTZi9979642Qs8+m+cV/hVInPvRrRTF6IYSJYNsySccSgq3GuqBjAbR0ihFMRAQKy5WhVoaKDZkMTOXALAm+XKtL8NUATAcSCmouqALoEtNBSScKuirIZARojYohr8SErZDshPgMsEJikMHBnH4YMtg0DjXEfxpH5t5sCfqHwMlBMiyMBzvuQRJl8eTr3urErEF+CuoZqBYhYZ/JSy68hFBEoV0oPgqpC4+tv5MqYj46Ab/bookbMhPFm2WWK1XhySdhZCcQBrNFZg7Tgto4pFrAcjVnLpYlgBqFK38D3RVQc+sw4nGQkkAF3DhYpgDdulBD+UvmsPca9G4oCuF2WDUD1HJDBtZEHUbqkCzJ+raQw4xoVsZBTZ5AcM0PqlU4tNTyiUgz0k4/eu57tD6Wl/DamQYzCT0pmGWC68BADqoZguVsw+DCJYAUIPB4/QEda3jvB+kqTANdbgnKBuTCYk9qCH83EoJwWXBkXAIvzQ/G1eU75XNDi973eeQZTYDZp3jZay7jsVt3Eg5p0g6s3aPZtB+uWFhgzeUuax/ZhNmcoFbPn6SCPTk8aOX/bSUoBNiFNHYUeR7+hMRhv21FAgU3ARsRg92FMAJyiA79gKiBGHKvn4bbxWAoGj6vEOC/vpH1n5tP74ufJ27gYWJP7MByH5b2+SyFxr7QcC6dh8LdTSTe+E8YsfZnpLZjSbUm7JXqIFTGIFoXmmClKh5tJAotSaFntSSk39T84FgJKhUoDUqgStfBqQpcZkRAeRO368iqKZmErBdv0FUx9tUMuAmIJD3GTBxa49Aeh1g3RJpF/UGcW+F7ITK0NAVcMgiWm6tDriIwnY5D0fQChBGoaDHgqTrEyhCvegyePEyOQX0QJvdupT44hp4/G+qK5NOsgE/K6FYysH4XqLJEHVVcjIM2oN6HdKiKzB4OwoNDQ3YCfvo7eGw/tJjQMwTnbkA0lXakRnw3kJRZcLwufXrHQVi12bvpC8JCQ3vclUFTld+oBNLxi678HUEG/ewi7NwHE3mUDV1uEF86ISkhlT5PRrwgC1ECz7bR+zGY9vbDKQlIGFo6bXZKljqHUINqBIbQ/zxMAAFA4A35sEOZIJjnOwMloaCNN8mEl0IcQdeDPnTBa3+FwEuz5K9bIkjKsJBl9xBikArQ6aR588VvZOKJr3LJBRDZC7f3SRDuth2wLVNirLYBwzSI+5DIcym+QfJ104Vw2mJeI1N4GSCI56o4dEIzQJ0FxiPgPALmGlCzvHNlvd/7UJGPsXtLBj0L7CkI+3pPcmgAzSaAhXxxDDDmNNyA1wwN9r5biJILEiB8Q+8b4AaIpPqQglXvJbzgUpRShzTp2YppeI89CxSEAph1oW5D2IWkIUMzpMC0xVAqLc5YrQC1HBh56SsxCyxDYAU7BkTBMEEZcr5aCKohwXRzQFJDvC6QQjjm9c9mSDZBPA1GQvSbUI1zrUZ7QQwbzSgGu3AZ1TBUhsy4TBot7cKkKNkwlYGYlgBa0oKekNiMiIaqKavN/gzUtkJtaICffOJbvOPjn6b7dBOz5/j6OymjS01oMNNUpSw4h0Inh4qvBRfy22BrVpS7eFKWHVQRQ5bxjusFwrAzA+tcSO+A65vBWg4sa4J6AbZVoNNbgiTlfAwjRuEAUgYZoLUO+brHhxI86GTJB8azxYQPF//3YURfoYaXgQxKz/sxTVlSlWyYyECxijw9PxXQRdpcI8hm8z1nA+mxjQE7CAJueOfyg3oxyRiKhiRDx/Je+bz3nLJMxz+nr6G83/qYdBhhB/j9w5T2zFvWS0diDpdcEeayM2ex/54ov/uP3di2xgV2j0Aopkm1hlD1k4hWNjAPDkk+8OEO//Nm77UL2VOjEWYocXQIqQXUBaC/j2R+dCGUsWHvvT8Z+hhuTdpvzUa8hyYOZaz4/aAREsD/zgJj1RG34FYLMHpb0J4IQQC2ga0A4A5CdfeFpN4lsILWmsrY/cQ6Lz2uCo8llgnhkDAIoiFhIpTqQgWzDGELGBHh3VpZiKWg4DFYnDxUcgIbpCqQSILRDLEOCVjpmOC2kYg0ITolsEPChmhVEltbopCIQyQBTV2QaBE2TzwKTQoiKgj1yGJS41ClissgsFXDiIZiCUpT4kSoGERTilhUQx2iNenzEQXNIWipQU8EkjVhaYQtiEZk8stoTf/j/8H3v7CG8y64lnMvNUisOI7+TkjrjVLm0CmzcVAfT+pAvxxacj1HzEU05SDeUatgRk9NSuysrwJqB9K5OrLyJEfkIY3XoZ6FGTaBx9GH4IgWQmL3qUFx5KmcoPgr6pN2xHyD6RtdP4CWariA50VWKjCek+h3qSIrCtdumNt8jNX3YH3j7Q96l0MCO74RnPZ6fQ/LADMBqQT0GPJshsYgk5Nja3mC5A3fyPpcHN/b9hXj09hMGVjROLzu4suIWHWuXDGP3ZtWMTongmXsnrY7GjHsk0MVZEur50gUwdK7UQwEJhpGJvylBGyGLMHE1ugaKlCrwbgX3MfBWC2YLbsRQ+0voXzdeu9VOxRzkGojWJVUkP7YGFRrnBR1QmbAw8QZWUfY3oLy4wI+hu/3AUfOqctQureb+PX/goq0i37zezGmPgSdG05EkyRj0JaS5INQGUoTUK6J96niEscpmrJ0TyjBQu0C4ELVWxWbDsyMQ1M7MNfTnyOerRX3Ygo1CczFJoVN0FSUhIR0CzS1QEsrtHdAIiFtNhAv10YWKjbBYqyIy4iGHTYMVaEwCeUJyYCLmxKEM2sa1xs/sSSEbPF2jaowKtIWtHrMjM4E5NugfT6MOWAVptj2+Lv4wfa/4anHrudD18w9pv6evdFtxMT8ZdEzMbL+rO80fOZZDD+eMf2P9o6bENL9zlqQi2BOAOsRIMlGBkcF+nJQ2CsBZNWKGC7fC6gjKW1lhL5TgFztxFlffgD8ZONo056n79FWmKbJ+Xxcw4MJIp5XVC1DUwLiTRJIKxcJlvctBO57jODpNtLD/CBbE6hOSVowDCiWwS6DGZP0zLTnsYSUUGjy66HrLBj2EwL8ANq414Y2ggCgz8goC1E+0iEcSnVAcU7HOE898E5+9V/388O7SsxoS1K3j+xAWsPSs1eerIaPjuf6n/v/W4ixfQwxuN2Il55BoIYOAgzc/00LGJeA+0NkNXU2YpwnEXaLz+k2mHa5VIsYBR0CVSOg3HkGUooMEHTMKsBsULMPbZJ2Ye8PMHQl4Iv7sQDfo7cAG5ynwlgLPo0183yUUji1HPbwe4i3b3x2emyQDiT5pTkNoTHxfaqGpOjmwzAYhlQMig5kQ4KLlrxgWFx58EMHtLVCSxdEe8GNQikuHjSmeJh2xYs32oKldoTE6022Q2sLJNugqUnOqRAOb4UgybPkPUJXw5gN+7LQPw7jU1CbkJVqUxMYTZJ5Fq1KYo+lBPasuQKzGTVhK4QtmQxUAuKtkK5DaxkqBpg5uHJqlFuzH+HGh77Gh9h1TP2dmKfrL80ao6RPJzGCTumL9/s8wsC5AgjVG8ZGDUpJGPE+aEYwIiYQ6CCCGOmSeGSDJeECqtlIx+9ruFYdsexhsBOwk4D19GzFd+xO2g9r5tA8fN9olYGYBB9TccFxI46kL0YisoyrKE8vDsFy1mcp+DfpG3CHANNtBrMdUt3QEfeWYRriMfFOkhFhmaSU4HDYwM8g/PUo9b+pwHVIr6kSGFffW/dYFpQRzzEHnW1w5SqDsNPKbQ+M87mP30z/gw4HhqBgw+6BzFHnbKspTWTWopPTr2+EGh/U4Z6k35dbgPmg1wMXg2omgGsy3vd+0NL7jToL1B2gN4FaAcwEPQCqjYCR4k/8no60zyipEowfH1M/3CmpA+ZcDgfCnNIk7tjdElvzg6EmAbTknUdPgbZfReTct6GUwrXL1Ps+Qaz17uOWcXg6SYRAeWyJel28xFBEVpxFA3I5KHh4bcgWdlK9BlZUYLK2bjBTMLNLmAaxlLTBbWKaQWPWxSMuamhuhrY6lGLC81UxcRaUJTxe25ImF7WoNeupoNnDmTMFGB2EyQk4OCaVA5witFjgxCDWBvkWIAWWH/hzhaUQccCqCmxi+Tg54tGTgOQMocJSAibgTXmHF03sPq7+nr3RPdyzfaaGN4J4dBmC9Mg2ICcz2m3IeF5KQ9DYFdc96/0b9y6Hg8AGae9cXlCsoMBt9eIOrcGJ/FtWHmY5bMODlpC5ny19wbdrJ4sHA1Ktqk5QDcaPZteQYFYByiFoMiUF0lJCXakBI1moTRFkmPkejt8e39DkmKYnGUmItUC8HdpjYiN31MQL0XXB08IGLFTyuErA2w34WQZ2tbWRv3MUdXE9MBw+v9jw3icIgoM1YByKSbiyZRH/fu8+Jm+Fe7c6lJ2jdBmvDcoK8erXv5G3vf9DLJp/EvQQf+b2daIO+66xH/uGdwEyWWxASqO1eP9XCJIfjIbfpEFdCM5vQF0JFKG6DpiCyAr53bRxq4MKS9Td/3/63nzWgs939g1vFYivPIK5UN/3AOFKv0zaFjIwNNPBTLTcC2MLsVb9E8oMo12H2oGvEkp+AyPmHpmI8Swka8NkDiaHYXIcKh47JmpCIiqerHbBsWA0A05GHIckUrugd6YYuq52aO6Wuh2uktVN1ZFgmJOHugNpz1OOaqikoWoJdTFpCsvBMT3vVkv1u6IttEa7Cv2TkB2G3ABUp2B4EsamYGoc6jlxMqIpSHRAZKYERNMt0NIibXKVrEhURR5FzhBsOQzTcJxKI6vSnHxmOtDzNNH5k8N0j2e0fDyr3HCsv+StEXQWLzV1C5LqezmSdNYCTFTggVHo8zqIvzo2QLxdn3zv/dmsYcSAGWHvA+/ak8jYmefdyj0atvkY5LM0unAo9/1kxIx7Y8RA9JUj8Ho8bLBuQDksHbAWgmoFylUvM6xOMOB8XTRSxWyC1GKgKQnNCcHeYsrrpFmPU+sZgVpClmtpZNn4r2MwfhHE7xxm8nZHDNMrPCXkCTKgfNzT9yxNuZdiFr4ztouRfS5qQDyTRlEmdM2eyYUXL+dAXwZ0E//+5a9y36Mb+OGvbuPT73z7iSnXN7T+5ODzleGQqP4Rx54L/B70LlBLEEtRQJwFn2nlM0IMUGuA3wgH1jgbwj3C4S3sBXsVpM/0DK83OUb9BArFkYWNDg9yVhW0dx1ys65TR+//MYZygtVNtKEN/uRbiKBmfhbis9FoagM/I2x+FiNpB9j7CcpgH+zbBcMjEpvO2B6n1RKvL5aWJk4WQBVB2cIuUK0Q74DmGdDRBb1t0GyJM1HXMk9ow2utKzSuqiMGPOaIJ23Z4ula4eBRFjWMuDBegFzeGyPDkOuH8YOg+8VByQx7k0BBAnJOmwT+3RgyYeagHoNwRRgadl2ODXse98GK4MxtZYhVhbvu34SymK48p2LH19/JGV0/+OIc5TufK+rP4P7VEgQFd+3guwKy5/IYknvTBhzMwxO2UCchsLMWMvtMnwdBEh5W8EgGLtkkdJLQmJzvPgRYv8yAKQV3WjBucEIG12+2y0nF4kTKUoGp7t+HRRCE8SLt2pGlTsWRdEXHRgxAXb4ngehxhKBUZBtBtl4DZlipCZ7tlmEyJksrN0+wHgtBpS4exDwPPtplQXkJLH6NyfA/OLI59gIkWm8QlCBLem3wjUlN7sfNwNQel/wjYGdgRtpkNOsI/QxoTrfy1a98hWtf8lKe2LGbd/7ZX/CBz93I7l33MztxAE7G6Pri69SHxBphhcOgBhUGfQHSaVoJ6ii7SCdqJUhsAOgC8yyw14I6DYx54qmZYRh8APq2w9LrIeIZWrcT1H4v868x8AmHTpIaqJtgrTmkWc5kP6HRe8WQ++3xnQefvjcFhN8C6RuEoDJ4O2bt3ah0ThwRhxPu+wCPPAHb98G+SaHB15QEnFKmsAfSdVCul82VkOSFaBNEOqF5LqS7JRCWtCTQZgFKif9QQxIpClWhb2kHoh6sU/JgN8f0Ek0seTSjNhzMwPgY5EYlcWFqGxS2gT0K7rh42/UC1LT8LtEigbFkxCv/6LExXBuqJTHwTkWMbsEWmCJXk+oEzVVIlSFWglQOUkV5b1TkWTwddHPy7IVjiY8j+hF6v1O0IbO+T3HyDLYG1iHGsxNxJqZqQSEuEBh3BPGENfJgNeJw3ReC7WH4XR2mNslM1WxL/Owe7zackFAy76lDrcFgPxvxb7sxiehExTkIbhoZCD5NyV9iRoEmocXELFkGTeY9ip6/hI+AahKQ3x7yztOFKFATEO+9wVypC7d6GptMIz3dD45ZELbFYKSRgMm8ZknFXFeuSaNHgVuAv5LjCXsHJpAH0Y8sK4a9/6dgyw8g8pR42RN5B9tTXDSZ4OxLruDqy1+CYZisOW0p5yzr4pGHbmZwzy6emhgTI39CyiXo3QYBxu0HmQ43vD6dTHlLxrOQ5IcLkMBaP8HqyWdnWPIbdQWo+4G1oLuALKh5Up9g5BH43u/hDR+C5EzB03mKoBSkz1rwYbpGiKGSkuotnmitqe/7PbHqWFDnQRFQ96Y95TOg+zNoQtjj2zEnP0yoKyM1H3zi/uGJH89C1m+D3RNwwBFnyYpDexPM74C5XdCVEm8vlZQCNHZUaukmO2X5nkgILhxRwa37UDlIllpsSrxKqybj3EhCPCS1dosefBAyYMqGgSz0HYCRYZgcgKkDUNsC7k5gXGqUaFsCmFZcru9q4Z1bpqy26ohzUyoJO8gIC17t5IU6NuV1/8mKUN4oQXxCsnDbsjC3CB01SKcEYlBH1ZzIyRldP6LbiCU2SomghpqftOAzCyYJ3NYG+GCQoE744Q70AeB+AmqlRpS1KSaQQaEGv3dhwAHlyK0NIkyeJLC9JuUJx0+iyf41/RjJyYiqQioq3FvbJjC+YblhFZfiIR0JiCekAMew5U0YXgKCqSVbzI4ixi9NUB4yTOCOFwmelx/4KgLNYLVKB44a0BEO0IqUgiYD4hWod4BuQx7O48jsuAZwwer2TjuJGOX9iMEtMI0p14agXmHawwWolasoLL7/q19Rrgzxlmuu4yUXJKiWlzC5exfFk5nV9iKzs9/DFQHc5TsAjYbX78c+Q2Em6CKwDdQqPLzLa9fhXvQc4DQvNbgZ8QwGgSwsy0P0Qdg+Aiv/AiKzZbmND4E1rvj8Z+MHVGs9oGZOX0o7ddj/y6C8Z6Ol8uMk5Qh0fxZtdFGb2I27681E5+2WLE4/ESZI1Toh6RuHva4skEwTuptgQRecvhjmzIbODglIxeKCi9ajQnFMdkAiZZCKK+Iob+6zpxGWsBbPNpQXipaFsB/cqOC0dUs83Iol3xWqMFaC0X7P2I7A2BDUd8tz8/H46YC5IVluZpNXzKZVstdCfjZcBGqOEKOsujAXnArYHsSR1zCWh7FxcMegegBSo9CZgUVVmGPDwk7o6Q72BziaPGujG2qG+BzxmmoZJPffT2w43PBq77sWBA9rFSC93gbuPk8pR7FcxxprOeAeCxZGhbNX1FAJw61tcMAj4e9HWGT+6s2/rRxicJ8r8eMfJyNGs+SrGyZkpggmL38AKamxULDkuIQpqdTTNCS8ILm/pPetpY/R+sviHPIcUkwvoa0ucFog3AodUa+UrAPlAgyEIRITb2C8BNVRUDmhjAJQAnUzxF8uNUbjTRIpr/UhXNd+goBph3gSuiYdt1FcLNY98hhbdm8jaWW4+bs3stA6yO2PlCmUTpRb4smEp4/5HFpIJkoQZzjc8DbCDQpYjAQbBhAubwYxij750zfWFpgvhdo/yvNRnUBWVgzsh56LoNgED30JVtwAnbb8XlW9Z+IHBxrhhRpebmwwQOoT+whNrkX5Honv7fp4tQMkrkdHX4xdHMde9zbii54M6GlwaF2PE5Q+1yurYUAqDYsXwupFsGwxzOqArk6TSFcabSjsWp16xMZMVUh2OcSSYQwVxVSaEFWq2NNlRwAiZW+Zbgp/tupKucWClmplZgiUIUt9OyT9LlKSDLFyXeo6TBSQ8VEJ7lmZ8mgjaQglxSPt7ZYte1rT4KaULI5cjSrJM4i4wpZyXcGuyznYOwJ9ewW2yI5CdFySPLZqWA4M9cPpITjzOPp7dkY3BIvfCt3nw0BGsJNSEfITslRmGLF44wQeVhgZ0R7O6Lri5pMmINOVDr+QJ1FRvu/VucD9UZjbCz19QqKfMOHXdanDCcGqGiBkhonFQpQKxWfVzD+U6BaZOEwfZ7QQbqcHzeiceMHVgtRCsOJQ86lIJuJq+HquICuJKuJp+Uv/EtPlIuNLwHUgOgpnz4LNUcipgPVVLcPQPjhgQH+vGPx8P1QHEIPj44ga9CMQvx963yADYKIfWVIMIwZvGAhB4hxwBiUCHYjnTjo1olF43wfeS39fHz/5+jcI9TYxoyfMzj1jJ6dc09NDFTGe/qj2oZsigeGCQ4NrniFTBnAawi8sIcGG3QRwhUtgeOeBbgZ3N5jngd4KehScSWhaBC1LZSeCR34CXVE47WWeFny9+HEP/9nWgND5MkPisc6230vSzRyKTfsG1wBqrdD5CdyaTf7eT5Ce+4gQH0oE/QtOClqAoKu1NMGcNlg2C85YBEvnwawZMVqakpjRJnQohGuWcWIxjGQRK1YAZaAx0bhUKU4/Fq1lBWD4LBEFliuZYUpDpEmy12pKJra6Bw8kvWcX9hg3RhlcC3JxcLJMT2idC2H0ABA16WhqYtXi2ayYP5u5vTNpSc2grJNM2Vnyk/0Ui1ux6QeVJW2VqWJTLkI1Czor9YOHCpDNyPVMYJ+SLLd0hyKh1HNodB3YcQ/seMRL9w2DMQPcLlBngw4jXsQBYCsy8FLIXY0DEw3Ra5/oeix3UUGo1Ysu+p0GyUr7wTisqctp99Zgr+8lHiZ1p45WxrGDfS+wuDnJNJseeCbBdjsZZI1ig+NCxqdkVQk8V7vhr++Bgeg8ReCOG2C1w4J2UI/B/n+CpS+H6rvgkRyM1mHSBmcE3P1AWDjPVL37cJBJshPYzvQSIvMvkLpAnIr6VmSZMeXdS13Sli9cY/L4txwqh+vfiqFMGzNus37dI2x54inymSwPTk7yXIguenod9/Sz2NOfz1KIe+2rcmRqod8vfYPmU8l6EMx82DtnO9NLKRWG0AVQ+y1EV8r5VVKI/+X9kFwkuPwFM2DzFtj8PVj+ZoGHlL++9oNivqebaMJfCmqnhhq61bNOiNH3ud0h5CRNb8FhAVO3/R3pjm9jhHVQid73cP3J+CTGQ5t3ykQYTpsJi9thURvMa1Gk0xGMZBTlaIhqdDxKKB726i9CsESwUWRRKK/sohtMdq48EyMqS/9UWIqhV1JeIp8fXNYCX6SAuCFVxto6oLMGewswVIP6hLR1YtTg7Kuu4JprXs0NF19Gb2cP8VgYwwSNplKtkCvA9r1VcmWDbZvGqI31M2hvJBRfR63yFLnJPjKZ8nRafjyiwAHHViyfZ7C4axaJuT08Wa7ymuPo79kZXRfszQ2/1J7xtQhw21akY65GpsMCskTbQZBtZiDeb5ygOMrhYkJci3LLDR9rGw5MSmFtkEIpzjENt8Z1NIZp4DonG/Z6HmRS8KLpiL8/yH3c0Ke1+SBynsCzjSK6LiNGOMP0zr7Ty84yovOkkNdbbu7g8U+NU53S3DYOS98MIQfqw95uEHnEK8oxXaxomnwfRZ7nTO/7Kahvgf2fBPcaBEOtyLWMJoFO7AfggU85VA8c3nAXXBeruYnRg6P89Fv/dSQ0dZIT5dbvw5JrBVuc3rp4MbKS8OmMfrAXjtxHrNHwhpE+XUB0nm3Qkz+5mWCsgNovIdwH5kLgAJirILkbeX7LYGwQWk2phfzobbDsldA838s89PFdEyiL5+yLM76fyMgjYpD8a/pUQxO0XkQx8TEmfvU9uipfx+x0ZWzZBIHDKEFiy0lEcxKGUK7a4jCnA+a0wJx2aGmPopJxVNLDsaIWKt4ChoVs4BZBojE1NCOYxIEKStcO5VO7wX1HwkI3a4pBzfQWxg5E8xIU1hWJizQlJMMs78ouJzOTBltjmt27NPW6IhxStITqXLrqTOz6fnbsvQ1XbcUpHKDqjFEql6hXDEYmNeOZZsrlNozEMrKTq9Aj78KqRAgX9qNH7sGe2EBrfJSPfPbtJJwJnnj4cXbuG2KXmebgQZP3vPPDx9Xfiavex6EMggGaQarob0E8o8XA6Qj3sUjgJTnesZWG8xwujuRrh7Vso9EomqAmzrQYiMEpc2iqmdaEYiGqtZMmeD334nNyfePmZ7b4mKHP8vC4zNOYo28E/Pb6NDFfl/45p+T7WD2E/neXqWiC9EfHGbvRZLTg0ObjxyXv2AxihHxeqoLpLeI1qBmgPWye/5TfuT8lSIxQoNohHpZltR2B8kYIx6LYZh23cXZ0S9QnjoUrcdIrk113QbYfVr8Oom0IrlkEFiIee5ygEts0HkVgzHyowZ8MfW+4hOC7uxA9RRp+1wmR1VBZB4l5SJnAceEqDw9AYVBWGzta4cfb4P790LUJXjsbXns1dF4sl1BhMIqw3dYM5F06LWjedDuzw5Nyff+ZeJOHVnDAfAsb/ucuLtny10TeUhFc0jeuvjfsM1l8auIJStWC5jC0NENzXOojRDSouoPCAKtJFGgi7qryPbME/uympmdyj3zpTzi+l+/9xE1CMSJxD5TYA7MOZkVYCWEXmiPQ2QSxiiTjtEQtVp57EVOjrezph95ZC2hNddO/cQ8qfztuYS0z2oaJG2PUC5OMlioM1mS794kRGOiDjAOTpd8xtMMiNxYnYi4llVhMR9Nqzp19A2aTyfIF85nVrbjjN59j7ZYD5HIbWLlsLle+tPW4+jt5ytjRHEgXYSc8hni4pyEGuAXBxyYJyhg2Bi8aRQuvLmVA1o8s++JH5/2BGUNoPS3AJvnItAwc28Wt1LGdk+UZPE/ie6OagFpXJMhO8z2uMe99MwF+FwHVIZ3RKSHe3CiBF1aScxtPwPWLb6Bw7h6evOBJRvdrnK0O5g2wvw6VAbwoI4GHnUWejc/LV2ApRWSpQbHiiJ6XIDUwSgiFzBvc2gBnAdgzkYl3FBaffgaVWoXd6zc+j8o8VFa/Htb/CO7/Epz5Smhf7HG7tyJ67kE8Rt/DzRN49D6jxv/OhyDCBPTHmUjQMItMQlrYJpE1kP+y8KE3l+DdXwVzSvyQ1gi8Kg03j8IBj+64pwgbtsGtO+G1v4PtTcDN8NE3w29HH+JL6/6V9o52/k1/kzkzG6AFrw8QhYlikq99a4A/ffgLtHwsL+30J2B/ktYNbfK9/BOUUF0KwkQM4a9W65KhlijbhApVVLSMjsVBhzzj6g/gNEEnVwSV9KuS8+vogNHhJdzUopJpWvO84LotsZxiTQLIKc9QF+ImZiqCjsS5YOnlzJv5blLLzwYiKBSuk8NevpZwbBXwFtCbwXmY0a2/ZGJ4B5NlzUAWnhqEwUnJtDuwBaqTNqaRQ+vHcJzHMEO3kAy3Mm/+WXz2nxfRbc3Arb+DC7pX46Ru59XXzaSpueu4+nv+eLoQELUfQco0rgSuAnMSrINSsUqXORQ/aJC6Fs5bxPISphoMbyIM9TLUwogBWAhkwXBAK0WiKUGxUMGp1XFqf4SALgRLKZ/KVSHAGf22Vgkqo8UJSinGZD+qSEQCk4Wi9FsmARtam0zONlfz4MObeOK026idCwdzGm5Hio2vhup2pouL+0Y7vC1KLVeRbZPmItS1AlDWFB90ZLWSAwqQSDVjxixyYxMB7GGYlDc6wmCYJR9t37AJM36SPKVnKbPfKvUlnrgR7v8GnHYpLLoAQnECfvgsaR9hxABnCeoXhGjIxEGejZ9550eS2hDd+QbZAhZAdBHs/AG892F4ZDLwK+wqfG84SPbxpQTc5cD9W4OSutl+aJ7zMAduXovRZLH4b2rivfpeblRergE/+UaJF930bRZ91JaaET7G7x/nY/t+HMXHjU9QEpbo0TFgJC80qsQURJtcOppclJvFtgtYtGO5FTCr4gGTIujwzdIYnRPM0ChK6lddi/ceBjclZSOzNSkqk3OFIjach3xF2B/tCUVTLMbstlUsWfximoxriYebsMwOBKPZAAximtsxYwtADSIcvwVoq4n0/CQ9tbuo7s+x7vEN7N7tMlaEyh7hxCs8OqcnTr1Ctj7Ilu1jbN/1WyKxFv71Uz9l4DaT3tRZXLumF8kcOracnNH18a+ng0tdZBCOAb3gLoHwUogVIatA72N6ZlbAGzskyPSTqtjjVyXgklnwie0w4RHcE0q+q6WRQEcPRHZJ4Rbb1lRzBdlI849ZBpGBkIDp6lw+9mYQ1KirIU5CEzADos2yBYpR8ji6iukIdarTYKFuZ+BrJZa/dgmV7/bxkDOKk/HO9yTS585CFDjFNI64OLOCc1a/gpu+9kXqT5YlIPp6sB4KUb+jfijLRBlUXY09kZletqqwIhyJUM2X5LyeIbarZezqMWbWw6R7fpLsWI1yvvb0Bx9H1GIpSn3RbNj5Hdj1IExsh9OvgKZZoLKIE7AcMbJhT7/jBEk9frDN93y9jKhpGKgbMdS+t6skWaV/Cbzj27DWgy38XljQgkgcSxpRsQe3wwWGRmnNO1fU6E4jOu0m6CNRGNoFnT9wufQSF3WW913JO1ms4cRRgoweP15wgtLTBlZKMNapMuzLACmwSzCcyRMijxkPEY9VSYYSpOMtWCqJ8gsUYHgK8/imTkQKsDgadF3uLQ5FS+qkTCHp8BOu7K82mJPtdZodqKUtls+4kFWJPyGkrgGVQDGGdn6FW9mIPXg3bmGYSNpEzVmFUvPQ2qU8vJtCxqBih+icvZS5S17JPbe/jbG9k7SlU0SNcQ6EvPDGYV0x2d5ExC5QybtUqqP85ObP0FRzWHjuBWzfNEjH9cfPAz5xo+vTZiwCztExRCE2I1sBdw/og5BfCpHloJZ73u5BQMPsGHz+7aDXwqOPCFb0+QuhaxncOwU/OSjndBxvhdSF51EpZk7BmK2xkLJy0xf300D/2GQjMoh8/qsmWIH5QTCfQgPThVFMS3Y+NYqy11TJw+yWLG7m0vELuNR6Pb9+0xd56ppfMhgpYo5Kyi+PIYb+LyG8CIyC8K1VGPTnYWTPCD83vkq9JhFangA2QXNLM+OlQ1NKQsk0ifZ2Mvt3TVsVK2RhpaLUiiUiFlTLBvG2dooTh/t2x5bMSJV69TlYmeRAtUD0Oli5DNp+ALt+DU/8EpashO7TpBwfFcT4phE9tyDea5lgKe/joH5Gl9/vS0hBj+2IZUiDjsK3H4BHKvLzVHA7R4hhRYgmYpSymSO+25OV8y6Mw1svQ7xcHyULyb1qoPCfcH4Cwm9EKn/lYbogvg9P+XCEnzquOfESe0Bbi3igNUOSFgazUApDvwXRao1kK7R01GjvqNCebyYZjmBZhsfB86OT/lLCBWWBignwHjHBsdGmy5TrMhUWumQZMbpDNhTrYpsVBnPTy1gSfy0hY6EYdT1EvfBtdtz+PXbdP4FdsenogLlLYNYNczGb+2BiPff/40G+8DNN1oLexWEuftHjdLa3UJ8YY/9okagLeYeAdaMgnLSYtSBB84wiiZxLdgcU8rBp7R28/0Ofomd+jFUveS/qaYq+npjR9bGvUMMLjmnYXmLAv7nwD8DPgAscKO2CDQUE612ALKGn4GwDuvtA9cCruiQXekYzMASLQrK0qcahVvS2//Gi6s2bTVa1h7h/pIytGnbkjBLQa/7YHN+DSG/qICjG4kdwC4gHVUeMQRHZz6sCxRGoRiVaW9OQaI3wopaF/KnxGbaPredu/R3WvXQXhWiJnjjMnQN7W0B/RxIieBmkOyDRCVPNYD0CU4+GKSwdI/V1sP8OnDuE/F6vwfhog8FVFoahaJk1l0oxQ8vseZRGDlKtVKkX6ziVDMvPPIe+PTuo50t0zJx7dKN7DCy/4tafG3rfPqBNcG+1BHo/AW0Xw/4bYedGyA3DnMWSbkoByTrrRLzDdmRVNoZ4v0kCbNRPBvIxdwfBd/ciy/06PL4psMt2yCBUd0FJpa0YQWxUO3Vqx9l8c3AKvnA5zJyD9JVO7z48mKl4vzBE2j8uQU6KBP3FH6O+waXhL5yUEzJ/DuQz0KeESWBXIJfxoNgCNBehV0FWOdRqGdqwsJqasSIOanoXTR+PSntE9RCYBQlSKAdXlanqglBGkUcxqKAagc4QpIwEi5NLuWbG+4kbp6OIo/V2dP5mdv3mJn7+1SmefAIWdMPieVIDJN67lY7LL8LelWXXXs1Dg3IXT/TVuPWuJ1iyaDG2aiKfn/Rzj1AmWDMgNQeaZtksn13AKTqYOWiqejtPYDH39OVY+Ryj+zbTNONFx9Xfsze6IaTn+IkLfnS0Yem5PAyLLfhFSb56/0xYNgh/aUsa7z+thOE4vOEhcEcR/NDrNMuLYGwF5sGfh0FnQG2T9MCOKvzgEvj2CDy0G86z4FGgtg/eVGqhq1bjvlqZjNYynkPIoDkWQ+Jo4mNdfwgDPUqQT2wgAyGPDHKfr6u99z6tzAuy2W1AF6xpW8hHOz9IdHgOw/oRfp6+mUfc3bjjUmwkFvOqPynI3Q/WyyXIlc1DNAlzkrDvGwq36KCXQWa73JdyGjB0pTBME9e2MeKzmHvWakItFn3r7icci9MxayYDu/YA4DouualJ3vTO1/Lf3/kBQ/s2B+31kzf8Je7h4hux50D3ehNS8akTmIlsgX4VLFkF3T+E/TfDlkdh9gC0j4IxBuoCpB/6hncQgRtKSD8KE7BKKgTebwRZLWck1bk4CrYBqZZWmqMusUqO3mScgYECZSfI3tXaxa4d3fqdFYZLU/DmizxMvTGAlgJ7HPq/DDPeAtGzQfkG12dSWN592Q2/9SYznYPKZogtOTHdnnU2DAxBuChw31hRMra0DbYr1bdGNISrUC66TE1liJkuTVoLWGulvN0womC0AXEwM5KyqISo62gXW5XQyp2uJBYOSapt/3qL11x4IWcvfCUhI4miDvoBnMkH2HTTbWx5corBPpgoQ2IAOuNQboexezLEWh+jRoTCWP6QDWS0hlyhSDKZZmrqMK54ARIlaG2CSNohlpBCVZEqhB2IqTBrVvWycPErUJZ6Wrz82RldU8oRGglZktoIkH/INjDAq+bBn8+Dtt9KadLlrhCeVwO3Aqt7YKMt1YSqOWR51izpeRflkSXbAZgzinTmGDAEb2+D6Bz41QQsT8GPV8FbqnBgv+J6FL89UCMVDjNVrTE/BBPtUk1ruorW0w1mA/EifFrb8w1J+EkjIYLdI2oENSvaCGompAmKo2hoaotzbe9SXlP+S2r9Sb5d+kc2dGzgQK0qSSoFyXjLKNnTSa8DY8rgvPeF2RCuUNgCE91QexBy92pIObACrH8He52nKk9fhmGw/NyLqZUmKZhNlDND9G3cgV0vEdYmkxMThzSrb88enni8hdUXruGxux9j8bLTiMXDbHxi4/GfwfF4289Sin2SxWSNA33I5pGzweiE9AdgxWUw8nXoe1Dqq84aEq9XXQxqDgIz9BBsqqkRw+sH4/38cj8AmgJGwHnCKwnoQiFfoCXcxGuuOZe9+RoLZlTZsm4LFQ1DTxNvOCsEHZZsSMok0j9cxJu24cC/Q+t8SF+LFLLxaVZ+CrnP1fYNtZewosdh8laYfAwWveXEdHvupafR25ejs3+MgUKNvjyMWlBthnAzxJqkvkE0LgXBDRTVsoNTD2HWEygrIQTckOtVCffpOM34dTBdVcEiTNqrBj8T2LMXBu5RXHzaAlbMupqQMR/FLpzsHdSzUzz1m7Xc+Y0p+sdgKgvtJgzXYU5F4iCZEdj/zVGIw8DgkRUIBgcPHtFW7YCbtxjYZJN1oDcGnb1SDyVpSpW+jnlzWbR8Nth5Ru6/iagBrZe/55j6e3ZGNyy0OysMhqVwtA7w3IZgWpMj9TK/YcCuFLR7eG3cgvNsYEgIzH6CFTYwITsjLAExRGOC2xptktmjypDqgvIjsKUVzmiGnkXw/q0h/uuAy8ZKlt6ZYRbvLbPUhK+tgu81w6f7CGhAT2dETYLgg82hyQnPh+ebRAb1ALJEnY0sIRtZNWHE4M727qEIC1Od/PnMt9E9cSlbMvexp/MO7ghvplRxZGeIFDhxKb5S1OJ9tP4+Ru+LDNpPS2GvH4ZBKOWg/EXvehcDd4H9+JG36ToOZ1z6EhYvX8Yt3/l3Nt33BOAQiUdoSneTGz0SPlj3wBNc8bIrmLtoMV/4whd44Im1bHxiI4ahiIRMytWjLD+ew2ztmx+Di3uhs1XSp6MFCVyrOWKAQ8ug94vQcivs+xbs2ghdY9AxDuaLBJKgHYkZaIS3XCXweP3+5Hr/DyM1KWrQo6A9Gecv/uwGYmYrt//3j3kym+dtL1mNk4gzUqswYjvTWcQmEsuvmoIjKqC9CJMh+MVv4NVnguFRP3Uchm6CeB90/h+keI7P5/a53j4ObTLdd3UNnG0w/jMwW2D++05ct6mzLmNJ2wCdbQfpGj9AcnSSFleTSwlPO5KWamJtYUgYBrFYjJDbRj2fQuk4ZjQFZsRLGyyLd2ua0kgFUCWEQRIThaaubcwSjD8OTYUWLlz4KlKxM4AWdP2/mXjqXjbfPchD99jkRgSl6E2CEYf1EzA0DBuehGUrhOb20O1w17BA99OiQigc2QrpMHE8+kJxCzw6rohfpDn9DOjoBroUq172XnCb2fnAT6j++mds+sXjvHX/c2V0TSFiaw229uyQw6EUJ2B0BBiT4hRLz0YoY0WC8n81WfoePtOYNhgK9vXDHD8CO4Vwe7X8bnwMhmbC1ct6qLa3ETmjg76+TdgXLOCDnRNkJvK89vQm5i7OsXASjH5wGwfJ8cTHp/2lmOvdd5lDavc+Z2IhhncS8aaiTHNbqROEupvkj1GHV89cxAd6v8n4qM3+5M+5qemH7CrmsDWyU0GLcCiLtlCUXCBdMpk7vIDtl2xh5KkSlYeBOOg7QW/z2tyOAO5Ha6MZ4vHt49x77zcZfvwhsEWRtUKdge3r0e6RHdV1Nfff+QCLz17DV/7jyzhNceLNcVStxosunMutd+3BdZ8/DOddv4OZMXjHi5fwso4i3QODpNtdEnlQYxIzUL2QuAFOWwNT34LBX0D+JpjdD5FrPZy3Bwl2mohhrSIW0ue+OgjeioXudoi4mre8dRWTvxzil//1C+Y5BkP5IkXH5b9+8yROrU7NFfgrgajez5GJugFdWCn4yNlwMAe7D8KiLqAFJu4B+1aY9TEvcFYigKjCBNxiP1ZVF85weS2UNkHrZRCa7f32BEWlF2AsbaKtJUFs3KB1EvblpxizXCotEGqSLLKOiEUnEZJGmqTRhUkbyo1CKCXVm0IxcYktnwDtAgU0k5hoUihChInTwhMbx3H6NK9/+WXM7T4dnLvQ5V2Mr7uXdb+Z4NF7XLbvlY0/u8NSXrJ7BuSfgs0Z0DuBNFxyNSychNyeQ9vU2dXB6rPP5ne3/eqY7XZsGD2o+cXPoNQHf/0+RXcXtBmPM3rnIF/69CYW9Xax6vo/O67+nrnRVaC0LG1cCxw0rr8Md6XK34XAd4HhMlJsxM92ShKA/F4UQaWOvEQCcTR3ObDegZcsheRB7xxRId7XZ3bTXkqhVlzIE9e+nk/9xZ+zeyrDWsNi4aJLOfeVKb60Nc8XNpbJO3W076U+k04WJghC+HiYn5L4fHi7DsEWMFmEVmcgSpjtHVOR45qyYd4x/xrW1K9i566t/Mz+T9aGtpALOThaAsMGUqeiUPIYCSVwxqC3by7ULKqdGncfqIQMRH7jtWkZpJOQOVpEWyk65p3Bq65axS/+634G6sHMdTRj2yi1ao3+7euJJi5ky923U86VUMDWA2Po5xk0d4ADZfhd+nr+7F8+hLHrDp686ev0PPA4c+e6hGYCwxKAMnuh7W+h6VIY/gr03wvdQ5B8KahLEIpdO9IvBpCVWIv3fwGpwrIoBv1F3N2KJcnNLMzbjOZkLh3yF4SVGgpBUXx4vsuSOsNtWdikxdFrBeZrcLfD8llS8EgnoO9hyP8Ilrwb1FwOqaI1nXHmG1tLMFY9BuU7ZbXY9npQEYKg7YmKSqOiRZjVSbytzNxJm+a8YtiaJGu6qGZFOhqlK9pK0o1h0otBJyrVIa0PJ4TvqLQsDabB8SKBZ2SSIIqJSZoLOT9VZcE5k5y++DpqQ//ByPb1TPaV2L3eYfta2LsFdlRkN5RJDXPTkhAzsx92ZGBrSWrfDo7DUxNyDEBzcwfFYo7R4UHuvP3Xz6Dtkgzy60ehqS3E//1cilRkH/SmWD++jYWXvYzzPvyO457iWXm62vHA8jpBkRYXLA3vV/D6KMwti8HUXjCGrQSUqBjTKadm65HPvgOIKri0GXZn4bs1eHlZ7I+qQWEPfLW9yqKEwYsvuYK//MinWLdWpqxf3fwwd9y6jisuXMqL3/ZxdOIglbt/QPjBPqr5+jM3mD57wAfDDYJg4dPxkZ+t+NllPi7uedXWclkCOoA9CJ31FK+KX8x5uVfx+777GGi7jbtGh7A7ZQllpiDVKjnxmaKMudYk9BXBGYezhs7lVz23Yc+BmVmDQt5g4k5bJkYToq+HzIaj36JhWZxz6XmsmRPl2/sP0NzeTbmQpVY5Pu82Go9Rr9fJZ4qM7H+KhYtXsPnJx9GOZtfO7HOnw6eRpx64i5sP/AXLFr2eH5zdyX2fei9XrD3AW5bDWYshOR+UZ3xDZ8Ks/wvl70L2d+D8F6T6wXgZqIWI8zATmRyHkP5sIIT+tXlojmHEbAr3ShWq9Tqo3eyXkvAWbMQRe513wSrLUtdFFjUTwGeA1WUIZaROtPEQbLoNLn81hFYhWWd+QDJO0Je8Ea0L4OwBeyfEzgHV7TG2ICiSdMLSLDdggkrmwRgnHWsmbGoqUQcd1TSpZiJqBtCCopXp2q4ARJGdHbV3M35DJKtDodA0oUhi6DSP732IBS3XcfrSqzHMtZSGNrPul3l2boPH1kG+CH4l0AjCErErkrkWTcqZRzTc8xTsHYaHx4IAWihsMbdrHnv37EXr49NmQmFYsjxNaajC3uEKP769hkpqvv4fM0i0zeDaF13Ei1/6EuLdi457nmfPXnDF+Bree2zocOC8WYKdvWsnDLugJgm4MXGkN/mTWAGMg0cG+abtXB3mV+Ctk/D9OlwGnObCpwqKL2WKhA9k2fNPX+WpDRsO+X2lUuOOB3aQmPsgvdecz6s/81P2/+I/+drnv4HzTAreeMGGaRK5P0p8lobfUX264clKFJmQcgQMBQPMmERqCzlo0hbvjl3NvOSb2RH7EetX/4YtEznsFrlHNycBYdOE+W0QbWe6En4mBWZrM86vQmTuzeHcBv2jLrrsetFzIA7VHmR7jcMkEotxw5vfwjv/9LV85uMfYXxsnMuvvoqH7r3/uM0ywlF6l6+k/6mN1Oo2Q/0jZHIuqY4OKtmx49KknmspHFjPP3zwg4Ra0kw8fCvu5DDfAX68H854EF6/AK5ZDnNOB2uOQA6xd0BkMRR/DJnfQrIPwq8AdTZiN3qRCWvE+7+A7PQZN6gOuXxjg8MuRwLHdyFdJYZ0n6r3/+owPFSTXRCKtaDL+TTbGYDVDB86CCtduGEKzr8KkucLDAoEKzjf2Ea8ZzoOtY1gpiFymax6ZCcKguBf8mS02kTAnXNQ8ShGtIWkSpOc9l7DKLrxXCkC3p0/ysME1YQcTzv+QIigSKCpMJGrseGhSZz2h+i8eha4BSLaxdkLG++XjVUPemeII3u9LjagKQ8j22G0JFt0TblSB3j7UGCGlGGQnRpnfGwIIxQDbXklDY8uy1bH+ZdPL2Lg9/v43I0VxkoGP/15livP3sdrX1Lj4++/AjMRFXqISh/zPM/c6GqmJ6QWBz5bh5sqcK8BZ3TB3MvEs40NwbwswdLH7xgLEO4kQA3aR+BSBf/TMP4OIp0wMSnFi5Nl+BNXIIvHY1G2nLUC98F1VOrw5GOPHfU27brNLd/5Prf9+Idc86ZLWbXmIi6+bDb33rz/6dvoNLx8o+tHOkINn8MzY0M8nfgpmlGCQvDjUNsL9V5oc+HK8V5KpTS7er7Bjc7vccJ10rOg2Cz4FTVobZUtRSplSY8u1UBHYEYceqOreeLu9Tgb5JJHTD154EuCcx7enJmLlvLWt7yRg3v38Ngjm4jF4uzZvZ/y0eoTN+gj3tHOK976Zr7+iR1QqWKXXWxzhI6FKxivFKhVSsfXXStBreWTFdch89DNR3xcduGRKXh0HXxuI1z+O7hmHqxZDnPOhOhMSL0HKrdDbi2ED0DqelBXiGGmF9k9YxMQDaHjFvr2PMV1cHoYFlfhQYJ52teYTzCwLejSMOR5aIcnis0GPtgH1/fCNXNhxkIIL/cCZzlk5MaYriGjFTABer8EzcIr5TtVJciw853JBCfZd326TUw8Vt2CMixphfJxuQji3fqgYYzAe/H/txoa488clndzk5Rt+M1dezDzJjNWtoHajx5/ioNrc2SyslnrONJV0ki3mReCC+ZDa7dwex/bBgNaMHKtD0VkVp+3hmsufwk33ngjHT0dbNqw8bitnrWkldlNNRZd1UI8nWbTvihf+dFOPv8f22lOWbxk5S5CTjNmfQqzYZulw+XZeboeYL8iAX8yD166Fl4LnNvtsT4yBEGGCOgB+Y1qRnSfQrBLLbPS37XCbyeCLdbHEN7ttVNS1pGq/OxdCv55xSzqLb3ITmrQ2h6hUnIolQ5bJ2kHFTKo1lxu+dZd/Pp/HuPsc8JEzUMVfsz2+fxLTeAV+BivHzSE5wbf9RnYPl5cQNLR98j2OYv2wa71Lu471nPX/vVM9jhEa1JmtSMJoZDUmpjZCgeHYdMOUCXJimpfAG5Uce6TS3ho54+Pext6/aH/NzVFKJYcXvKyqxgf7ONjf/kxKpUq6bYYE6MHUIZCHx4Ea/i3OHiQm77yJVLdbZRz8nQrJciMbqNr/hkceGoTbv2w5+bXNWhGdhseRzL2TlBWzoGBCdlJQDveJodHazswVoebhuGWYWh+FJbfAi+aB5efBqctgoSG7ANQ+ga07YDQdaBOQzpxC9BxOlSHYEWe9mXwRhP+8w7o1vL1lHetOEGZkSdKwWLb9/dM4GMhOGDDTzRYNlw2CFZR4A2V907QitAJI0JtdQfA3itkgMhSmRSUD1mlwK/cpXPgDAuGrbpPXLfBVOIihte36H4KnOPdZJwgv9pPq/SzNnw8xE8X8T3gJFBAa5fhkWGSuSRXvvJtNKfCKGyIdWDVIDMJB50gsc5FkJ9VHbBqpbB3dBUmFeTDYWbNaCe7fzBoglJseOQxihMVZs5eyq7dhw2Cw8QKW2x+tMbnRodY/uIOVi9ZwcvP6+CGd7yPt77pc3zkM+u48eOtXHz6Kpwnd2CeP+/Y53pa/TaKC1SguyZ7Hs0BvmxKVgY7kZl/EknLzXm/0QgNKkNQk3QKcARCWIRvRmVC/jJwhfbsnLeEN4CLV57G3VOacDSG7UaIpdqoNxRqjcW84sY1WH7xGkb3TjKyfyflfJW1D7kkmqDydN6pS7CTbSOue7i36xPOT9bw1r3zVAjWnR5+fLqC9G6w3pbnDnuQ7IQDIbCTkC9IebvSlBTlqOyVLafdca8NPTBQg3B/hCdu3UOp9Oy4WI7jkmxPs2XjBv776/9BPpsDpajg4pZrRxrcw0RrzcDOPRhWwE/RLtRxmMhvRoejECoEYy2JbKuTQpyj05GHPuNZ3fYh8vCtMFqAqg2lEXh8H5QKMLYLHtsreN/UOPRlJQnEqYuBnnLhwQw8uB4+t15WC+e3w5UKzhgHfQskN0DyDaAuBTUfmHgCNQJqBHQfdDpwzUzYMwCdGr6FdP+zlHSjzR7WmyUgHMxBdj+4LARLUrCwCA/ZcL8Nm8ah67dw1elw1TIwF3kGeABqw7JtTXQ2GB3I9t+N1DGDgDpWgfIEJE9CryI5BHmekpNOmz7fW4khBrcxWu5yKFfD92p9onMB35hr0ti6icLoGF1tSSKpMqHEamlcokDXisc4a0mBJ3bJZBrxzjhgCA97xgLZ5bfvUZhQcW546TVs2v7EIS1o7eilu6uNKy+7mL//+3/gNa98E/c9eMcxW2zXbIZ3j3LXHjhQnkv2Ak13YgdXXNfB5Ret4Zs//Dk//F0/K2eMk3Laj6u9E0oDtjKybFFRODcOesKbWf2oQQoY8mxWDxLxzRPs2eVJzBHWw7qGcz8M3I3kRyxGVpj/B0V5aACcFiLJBPVMmaHBMdyGHH3HUbiuJtXZzOWvfjM//dK35V5NjV13yI4FBeaOKT4FyKfgNK73fCPcGBE5WWkgrZMl2J7egBEL9Dtho53B9r3vCiRcqA1A2Wd17AR7isCBSHn3lgPnf+okXYee7h4GBgae/nZMCW0WKy6Up9i2fj2LFi9mdPQA47kCjlPHiiagfGQgLJ5OUcrkD/nMtQ/VdnkCVLxGOOFQHZMxFG+H0mzQSwhw7WEEED0JHSd7IelPngvhrPOF/YINbhkoSmW28RLoOoz3wfoBKA7DI9thYgoODMF4DW7pFxisBZkPLt0MV/8LrHgEEq/1gmwAfVDfA33b4IkI9LTAQ5PyWC8EXqvgd1pgBwi83suQ/j7Tkm3RNmchreDl82F9DL6xCVIHBU5aWYLOGEQXALMgvFCq8B2yAafPwGlcrXuTebIXmdiiJ65bufMiMpVkOXKjNn+y9cOGHgZCmsCvjxLUNvVTg02kA3dRqW2jJb2QVPpM4tFLUWoxEEWZMSJr+uh48Pect8dmdhUKGQlG1g0wkrLFu1WHzTtg5fnnkGiJsWPn3kNaMDk6wNTYQfbt2cnQSB+79m45opWppmbKpSK2baMUuFrq7Dqj67FHy9w3oPjV/b9k6ZzFJFJpHtsxyRNbd3L5GTOPq70TMrpVV5bA9ABFKdEY6fciqv6Kwh8wc5FIb5ZDS84p4ZReNQRfbzi8BHwB+BxwL/CkCVY4xDXXrqQ3Np9s6QAbHxzHsOKcff0bWfeLH2IYYBoharU6S2b1cN4Zq/lFKILZPIPLL5rHfY/uI+UOyuaPTyc+rOCvoHxj61tsl5OM/DZIATG4CYLtebz1aH8Z+ovCTIg1AUmoTMj2I9X9SAUwn3vk488hxAk5CGo7tG5vZ8HLFvDwg/cze+FCpsYnyWenCEWjOK5G2zbaCRqqTQsUpHpmcO4VVxOxM3Q1JcFaSLni8ONv3IxKJTDCYdzD0ldTLWnK2QJaH8dSVqG0B0ILHMhKCuXKS2BDG1QWEiSFIO2LzT0J3fqME8X0RKq8oKhpAlFobpYkHVxYuFASd7QtRljnYCInQa5NW2DnTnhgK+wehC+X4Kc5eM1t8MZdsOAySbpwW2DtFvhaFn7ncawLiBf7ImCblj79oRjcWA5gtQe8W3Tq8LUKzFXwiA2XjMLcWXB6BN6chtfNh9alEDuLgBHkrxYaIdHGQHCe6e2dFEht63ZOcp+0HEImzxC4Mf7g8Osq5AmqwdcJKoulCCLrVWRpPIUsd4S5rLBIRq4kMbcFxUtRajbTS0ulIX05S143xNyL91EdLbJ+k2LXToukMjl7TpVoq019VODmujvC975/P0frllpryqUyt9z0y6O2Mp/LEg5JkQ1DKbpjLjETOttCfOrz15OdmsvPvv8Yt9/9GOdefjlPbd7JV3++mZkz21nOG46pvRMyunlAD4redBHGbMFTAFkpDBEY1yxCLp9CcCiHoKRhWdTfuFIPIztpngHcH4b3dMJQfDb5uGZRzyhT/QIpxFJpSmXp2S3dreRHppjZZjFyYJT7fvhDCmP9NCVt0nMdohsiLOqFx48eeztUvGUYcOi+UjaBQX4uoAWQfjfonW810gf9NOCQZJTFohA1oeAKDag06f2mzztHMwEe2uZ9vgH0b2C8OMqvfvlL/v5zn2Pzln389tc/Y+55l3P++afTHElw1623sGvzDu9EGl2vgdYUBwd56pH7eOd73slLLlnJ3//TJ9n0+80A1KoViWAcJvbxEs79eh0FICr75C28EkYfhUc2AVcj47GT6a1qOACpxDHP+PTiPyffKDWuWHynwOdh+wbKlWCw8kLhne1yzLzZoK+Av8pDbhLGRuCRJ6BvB9w1AcYdMG8eTPXDD/pgkyt9eKPXnAngR0BUQ1FJemqHdxstCq4zYcqBX2u4vg5zNahZsHYKPhmFP70SWlqA0zyvOiJ6nC4/6T//GEG2nAsMgR4Bew84VYicDsrPsDsp+uMQUBTMaHr/n3Ev46KTYKAUCLywTqAF2XqkGTGyOVGwHgMnK9xtIyUYgVqJosNjQDRifTNR6nJCS0ps3fYT7rxlJ92Xvp2LXvoSettHCB/8Gqq+CysDbRasvXv7STXVdmRZ67owUhX+r647FHNb6Z1/Ae//5N/zutcN8b2vfpepvXsxVq7hM7/fxk/eduxznpDRHUdcbXISEZwKwcyGjsuoeAw1IDwIKoN4MAWCknNe+nAnQaIawBsV/KOW/nNNHRiGVHqCv/ryvYwtDjExUpdHMDbIjjtvpaVnJj3Ll2Kl9jG+fz9K59m2bTPR9FyiVoaYzmJnB9hehtmdsH/0aeylT3PzScRhDqWP+QP1uZAV3vn3ERTW94O/ZQ9RGBMs0i0ixtYmWM3lCTb+9O/bRTIAC6DRuGaIm/7npwwfHOBjn/1HEm1tPPTbW8gpk7HBkUPvx3MH3FqVka1b+fG3/oWVZ/wT+3ZuIzuRB6UIWxZ12z3Co53Y18cxJYp4WN5kQgx6z4fzXw83fRqqk8i4jYFKQKIZOudCsuNElOrJGEcGRBsnS6vhvQ8l+cbZh5F8SkFNWAMqAek0pGfDwtOBSXB3QeZhmJqEbBb+TMO1FoTPgJt3w8NZiQtuQzKtCxq+VICVCq4OQ9mETTa8VcFIXR7BL1z4hyWwcCsk65AYAuqeTRtDYiM+9bXTe/n0wwjBGNwDuY2wfyc0NcPc0zl0+54TFZ0RxbgWuJ6nYNSFKqYK3o3WCPLqkw03OAN0XIpA2xHZBNGdQpenwFW4iThG85koY5VcQ1kcGlwxQJ1GdeLX7NwS5qEDCSL3budFb/gbYs5OarmcdOMpKBjTvt0Ji+s6gp0qg6pts3Mc2meEKZWaKObHiCfG6GhZwl/92T8zy/xn/vz7X6XedPwo5QkZXT8rNqKl/N+yFyPrJp/iUxS7sAtYVIaIl1U1jZn6oqBDB0Y3Drx7AcRriHEZkr8qM8WVtSnet14e3fsBJ6b4cTKBSqUY2zdAuVqiUnfAdbj/nvuJprp5yYuvYP/2Ak3JASpunYnCs3BQ/cHayNv2l6rPlfQRhK0L3nXavWvsgepBgrquUWQl5uPMBodu8eNPeHsR4+xJZniQ/Pgo7/+7T3LZORdw4/e+zc//6ydPm03WMqOZFS9Zxef+7QuM7JSoaKqnh/LE6NMG0g6RMEFQZxnTW8Q/UoJ1FaguAdYCFwnGP3cGzGuC+RHZDeikxHfEfPGNKQQxHAiMsm74nUGwIajJNNbuB6WUBeQli7X1SijvEfjH6oSXrYGJA/DfFWn65QZcbsIDdXi1BXfa8tl8B/65Jhlr5yj4YAecq+F7CrbvgU+NwLWj8OkVEE4jE6zPry149zmB9CPfeUyDrgD9kN0Iu3ZCsQyd3fIdXk6CNo7kyT9jcSKyjXUtjHbrYFdRMResKkSnwEh7iop6wZ4iwSZ/hoCvJaBiQrENXW9H21lqjsLNJQnlo5ixKVRbHGU2zo6g3Ty6/igHdu/m/m1JLnnx2/jnf/8P3v+Ol3H1qiTveM04hgPObINzl7mcfwB+dwxYMRyWmce2XTGuxxKtOe+SS3Cyw6zfsJUoUQ4eOI3P/N13ecfb38k5kRaotHDdZW/kyV1PUV1w9nHVd0JGN+u9UgAuVO+H+GHTSRnZHq3dgW7fcB3RmGA1BDDLgNPOIKgfexeSSBGGlxXgXUhMrgu4xdBU3BqM9DORLWDik01EKvlhHnvo96xesoiFqRmopgSPbdnOMyZ/+jbJp3M9F3DC4eIg0IuHuVFF+mYTQrzH+36EoJh5FFF+BlFcGvGUfYBwLUdMDMowuf1Xd3HjV79DLTswbXDbOzp40XUv42c//BG1apAD3NKZ5F9u/Ay/f+xOtt27A6cojZ/ZO4uR2hgZR9LBn1YM5GHVCCpxGcBMYRVUx4DzQU1C8gkwV0BuDHJRGA3JdjAnLH7g3DemviH1HScf7208Bg6tJWI0fN840dnIBFgGwsIBjS2BxTHYfQdsexzuGRUnrgjsdWGDFjrkR034pAm76pDVMEcLPB+LQetc+PeN8JGL4L/3wfkOzAx7FK/5yLPvIkjuSnv356++qgjZ/SDkdsPQfqiXYGY3tK8C5oBOQm0SBgdmMO8EVxI6G4fxKpSrOPUMZiwLiYxXKc+R2dOMeVkZPqVmBtN0HcX0ak4XFLro4LhQtk0q9QgGZZqiJSLLQtBaEAUTJjs1wD03fZAta7eQm8piWi30tmW4+swmEpk9XLBCYyWB5iWoisXpV2/l4n2a/E54cuJIyqhtiydlKYUyTGque9T0dNNSfPQv3sTQL3/Mni1buWBFD//0hZ8xI7mQz773Xt59xWmcfmGS9JwOXvvmjzLrsuXH1d8JGd0pxJmaqWQ5tLEA5zdMmxqxEzuQaG/3YVOq3ywfZfDH7/UWNJW9CyS8VwEoyRh5H/AQ8Hng4SIki1Vqpj3NOjoDiQz79nJ4ZJS7JyZY0tbBnM52wlacqmmDc2gQ6LjyXEEJRxOFuPh+YGMEMZ5+9DmFDCzf8Cumtz9HE+wiXEUCJVlIuXHq0RrlSmAV7VqVsf2bqBeL1B0HlMIIhfn8l75MJdHJL//nFuHeAMpUnHvDhehahId+8QBOzntaSjF8cICSrj9zlTQ+6DoSe7EQr6wHCdnXYeWZsO+vIbsKMp0wuAVCvdA6jxOnjfmzub/C8g2rr0e/TzbCRT523xj7aQyqNh7vIp3XgyWVDaYLi86BqYxACj0bYc1BeFUINpXEuN5fhT+Jwy1VeIUhj/zlCpZE4ctbYXcNHntIVuv/3imc7GhdgntqHgIG+9f2O36MaTKA3gm1DEyOSAH61haYfS5UVogR/v3PmhhIvI6ll/81805QteRDkHMpTE1R0wdpaS6B68hW6o4N1argGeEUwVYbBtP1ST14Q9dAZ2tUMlAlS8UZpVZR1NwkeaeIm9lJNryJRHuFB9bmuel7j6IH+hgsQSXdw8tvuI6O7iY+9q9/w+zm/yKm9qFmOGC1YcTHab04xbU7c3R3wIHb4eDhlH6tCVuKeDiE4xqEtUO+Uj3kGDOsuOa6xVxxUZjv/GcfN1zYxIc+fw2//CV84K++RNpeQv+eOkuWh4icG2X1iguh/fhriBMyuhWkGPlZWp57CTG+jWMsNRuWD0OsFvRzX3ynOEawCW0aeL0D6lGkw8/gkD1OPJiGnII1WvrdJqDuONNB2zJHOqRV22Hr6Chbx8fRhDFi7ejyFNo5GaTnORKfnxtHBk0T4sGOIQZpEGnkDALvtYDALglETyNMG91oXwqtLKo1aZthmmitaWrtoF4p4zqukFINAyMU4TebMjx061coFwMe74tffxkf+cjH+Yt3/QkHN2amPw9HI2ht0xIxGLafodn1x1oYmUBmExRob0Ymml1wcBYYS0F/E3ivLI9rwPA+4M3PWJuHyjgBlOB3vsb/G4PujcfUCLa8rze8fMPte8su4nX62LoG1Sr/to9BWxHqWVhYh/4pOLcdVncKA2JLSR7h6QbUTXhzAhLnQf9GydJ8fwHOtaB7NcQ6hX9t9hN07kUEkTgfy9egs8AByB30bjcGDw/Bx78CfV+A/WY7c97wCf78Fe/ipbPiJ6hYUK0duPV+ouUcMUujLAcqBbRTgnoYFY1BMg3KZzP45GFT2AeGRqOxixUqmUFyk/3YtQPUzBHGnQNkys088chD4KzHTrncs14zvr/C+UviDJsh9udrdMQKzO0YZfmLXkVXbxr0MDhlsA6AaofoXFQkwsJXP4Ed28KFGx1u64NSg4HQGpRSZIoVNBCx1PR87AKtLSFWLoMWdYD7fvS3TEYn+fOPrKB1RjNv+dOFDO37IJ/+v//GnSNf4Yrcv/LUrzZDu8N577yS45nWEzK6Gvgv4ALgXMQ58+KXGEhEtqMbLpry8NnDxCHYUPUJxGi/CTjNATXhHVQmSB7wFLUb2KLhz5DNfz+JPNK89zoQHIqV6sTOj8r1tItyXFxcKI1hxppwijWCEfd8YAfPQPxEkU5kAMcJNqMc9W5rlCCbL4EsK8uIsisIxawM6oDBwlmXsnXX76dLJobjFvWyQ6xnNtdc/RIeuPMOdm/egA5b2KU8N3/hY+AE3NpoIszb3v12Htv4IDse7ztkFlWmIha3mBp6BgbXX773Mk0rZAGy7LGAx5HC9Vlpa3YmuKuBh0E9DFyHFGLf/+xVOi0+ncZnL8ChGL3T8Gr0fJMEnqzvLfhJVv7v7YbXbIKU5X6mt2tXeVicgr0FuGkQzmyHJ7Ws1FJIv33IhlsVnGN49epLcF0MVtagqQ7jkzC7C8xzPV1kvGtkkWSSJUjfMZABsF/ohE4RkjEYyMH3noL7R8GY18NZ7/sMH3zDG3hZZ4wWdcKILiRnYBh7MKIxdNWRjdIqBXTNQrku2jRRRsS7sXbEa0hySCJFPUMxP8Lg1A6Kk+upVndStgfYnRlk39ZxjEqRnaPw+EHF269q5pwb2rh7XZ0HN4wRS6c4++wFXHzBlXTOOB+l+kC9Uki6/i6cagQVKRI/fRaregw+1rKTqc+VuXtQ4zQM90rDTuF1r+Sm/3XXbIc1V7fy2qtmEdZ9rHrRfDrnrkKpNKFQBx/+8Ic52Ffnxp9+jaY7o7xh2fn86gffJx+vcPU7XnFM9Z2Q0QUpHvZXwBeRDWaTBMHp0zTM3wyt5WAzhHDDb/0MxQyyk8TFCC+38ZjpJBdPAy7wUwTinIlAFwmCFbdfvsAXu1LCt9iBIk1wXZxSUYr9utoD+svPL4xwLIlB6AwJSBZ2IwaqExmBeWQALyXYkXYWcDZwHvArZBZKIEWIXJeRHevp6GxleEAiaZVCFTRM7ttONn8Jl11yBZlSCcfQTOzYdojBNUOKD3zsT8GO8aW//SL1YtAZ0eC6CpNxjhlvaDROCQJj1oPgn0VgD4Fx6mKaX29vFl1wEXAnGOeDsQTc1OEXeRYSO4nf+qIPe+8nzvjUvobY0PQkM4VMph6AOHOF7BF41zZor8CfmXCjAx8BVin4fRhmvhz+7Q6o52CTgoUKrktC6CA4bWCdg3gZW5DEEYPA+M72XnUgA7UpiCeka68dgSenwFg0gzUf+Cx/+cY3clVrmJRSJx5EA+GzJZpBxSDngJ3EjfbguFXAJhQDrA7E4PYiHdenfuXkb2IKHTnIVHk92eLjTE3uZ3BnP+OlMoVJWHcQos0mn3lHO0NZ+NCXhrAjzVz9inP50Iev58xVNxCOzEcpL0hALwH1xPUUVkapDoyOLla/fQt/GXmcyHd3sWF/hfZ0K9t3jWETxq7KSk+7gYdnWrDy4hDveteZLOhso1qawZOPlcg5IWJRg96OOcRSbfyfz/8DY4UyP/3NjWzfcx9ve/FV3Pb9zz8/RlcjmWSfQ1aKuxBMdR7QEwHVJMeZFaha0FcTtfj9dR8y/rqAr3mP5XgdoYIY93chwdq7ERbSpkiKhF3BdeqH1iivF448iW/JtSthZ2zQvnV4AbzdvJSgcxMI68AnLftL8rD3fwUZzFOIqxRDPN7liKdrQsgyyWfHCDUHATE0tHV0Ui4VueUb/0akuY1Zqy5i/6O/53C2+JrLz+RdH/gof/fpjzKy88hw7/ylp2HaexhT1SO+A+9eFyCRTj8JKUWQ/z+KPPQWrz1zvfd+MZZ2YJ7AS5cdgLErJLX1BZHDja3v1frUQZ8pAkGwTRFMNj7eHpd63cu6IVWGnROw4nS4cT38GviGhoQNf/0LeCArsYrFPVJ8u7tbMtDqA2AOip3jEmSgbSXwMLzEI50FZxAMFyIRmCrJrgmV7nYu/uCn+NAb38CLW8IkTtbggoDLeh7ENSqs0Mk0hm7H0GHZicA86PFEI4ixbSMoeDMlROjIECq9EaJ7yOd3s25DPzpfp1qBB/thbi9EXYcvfn+U/qLBy15+OR/++N9y+ooChopRd0KoaW/dN7a+mKJ84sD5KHUWKjbAFW/qZ/U1I0xM1Pjsx79EKVehEG4nrOFg3wGcuuBIhgHLVxtc9/K59Ha0YCiLcKST/h0VXvfaH5NU23j3hSt46QU9dLfH+OtzPk5kVxP3DN7EZ35xI1ctaDqu+k7Y6IL0w0eBdyroMSTDaE4UoquZdn3VYxCpST/Zj2C3A8gYvBb4KgG10KdSgvRbP0NbIU7dFGKc/9s7fRNQsMJ02eXp8m4+jHj84jYaahWv3kYr6KLsXfOHlizoB8HpIIgqaqAHwjGwt4KbIaj3txMxVA7iMIwzvVGlU3OZPW826USJrWMjlD3bOzk+htYaMxJh9ctezp7H12FXDq3FEIqYXHjVZfz8gZ9xz6/uPOqtlvMFZiYdSRxoFAtxNJZD5CKo7QB9ALgIzJngbPbu00YmlouRCWOAoOqcFmpn7DpILIfXL2tloqvOd/blecFFEXBb/VldN/z1WRC+t+sbZy3z+eB9kNkt/89Pwe6iBKBDlhSNH3VgbcbbRGQ1rD4dVB1UN6SiUHsc3H1grkLs1yUIrLARdB/SD7yVkVuQrcesOORysLtisebdH+Qv3/hmrmyJEH8uDC4ASY9OUQFrFio5B4EQPPKv8texGQLSu+9BuHKzxiTRVAG31s+Wx/rJjtYZmoSxSZgRgu37YaACy5Yt5j8//ue89PoVxMKT/Or7/0FrextnXfkB3Eg3hvF0+cwGsg5eQCgyn84u6Gwd5/wl/8nmx0rsG93Pi6++mnPOuYbbb/8h5cIoq8+OcOUNIc5YkiCmooBFfjLMmktP45tfeBkPfLOHC849m+4FFtZoieZcjX/7qz9jsnIFP/r1P/Olu+4+7h2dlNEFWUmd0QRXdkNhp+yPpqKIMWhBgpV1uBKBEia9361BVp4gHqwBrGo4b9kU/HaWKwb2SSR2sNN7nwYKaUhHJpgsBqptQjDlvfrpDG/Vo9nkIBV5bsoIPlspIIZnDjKgC4AjHNB5r4CxLEyGEW/G9558LDdLEJ1PSu3V0+ZFWJ60aTea+N1amUSam5txtOay172RqZEpxo+SY/6qN13Kh975fq599csY25c94vuIgsF9u1h5ZpwlrbBxlAA6OB2BOxJQV6D9zLJZ4HbLvYUiYO8D/X2EVzqI6LuALH88LnLnUsi2w8/7Crx6+1wy6/KynPlDS+PM74vT8L+fEm4SeAUQZMGGkeVfFHreDj3joPdBZR+ou+BHndDcDQNZWKvh9wY8NQwXnQlGD2Kr5oKKQ6gF6r8HYwLZZr0VWU72AvtBbQV9UHi3ZggML6p8IA/t55/Ne975Pq5siT6HBhev4U1AN6hFyKzbqAiYToTwuWHT0UlfeWWKlSnuuW0Te/fX2VOAQlZqi2y1YcQwueSyC/jm1/+eObPyVMu3s+W3v2b32hHu2lTnnCdz/N3f30Q4FBFG2dO2TqG8Fa1rJsgWYShbwa7Ab37xK9KtHdRrNbRWFMsup522mM7mHjRVlFbEkt088JNRom6SN3/4CubOswjPU+g9Jp3tBpFZVVJ6ktdYvYT08XGxkza6L1VwVQFaDkC7756uI0i1rgQrr7OAMQVzWqCjIFtt/UjBlzT8+DC17TPgJgPeDjxahbujFstmtLGzPMreCc2eGnTnIKbFA74U+CgCPfzV0xrcBrGLMFV8+uOeD5lEZpRxxPBaSKZkGfYOgJNGBvslBPu3VYDfIkbXRQZoBWpodvbswTWacIby04BJJpNh4Zo1DO7v48k7bz8ikywUM7n6dVdz+4ZfsW/D0bPKUjMsEolmnhjMMWIjOJJGPK7TEOO50/PKLa8te0GXgJpUwdLjSLpvE0GQShwQeBz0k9CvJNnpzoM1BmO7OSOc5I9GTA6l5/jv6wQ1XiLIM/ISEFRNOMh+CdlQM3SMQtoAoxuWlGCeA+GDYNiQnoHgs3Gmkx1Ui+wkoncjW/S0IYPJALUaCajdD+qgTNZKC547asd424c+wUu6W4k9pwYX0D6wnQI6ODRrzBc/Oulnx1iIZ1FF60nqpT4e+/UvmddT44HHFPsntbexjGJGfA6vvOBVfOyL10D1Rn7977/k3odLbH5KsykDY3mILc2ybsvtLFtwDqlEB6aRQCmTY4sf2XFQjNDdEuP87hjrxqoMF12mJsemj9z2VJ0PvX0T73rfQd77nhl0tS0mEZniLW8+k52PtfE/P/wOY0Oa6997HZddtpJkrJdydgejY7voWdbNW3tefVz1nZTRjRnwgR5oMZHsP38i81eFOQ7xFuYik7SVF3L9BGJw48g4bVRPxYE/aYFyGG4dADsZY07KYt64ZmlNWA9JV+ANhThPSxGn61s8B9lMfwgJASuR4MgEYoDDQBXq9yJYSplgY8QhxKl4NUIV24xQyjyYdecjdfo7MzDkUbzNEIYyGNiyld3FIkfDra969YXM6T2L97z/neQmiqJMPwjlUZEyozb56ATRJaBN4Bokr9XHFWugukDP8O5rJmKMn5R71DOBGWAsBvdhpDBBDplAZjGduuz8ElDgHoSNyuXc1ZGTUu9JSaOB9cWnjPmThg8p+Fi8H1zznToQ45mSV7gZ2tq8CWgYaIHio+BuhdNnI47hDO+83u+UAcaloH8BekT0PF1zAbm+8v7XBugcFIZgcTnM3EiemHZQ6qR9q0PEHqxiRquytW7EOLrNbRSNd0AGyODUd7P5wVvYsW6A++7TzIw2EY4WGLYtVs68gstW/Rnnr5rBQ9/4JP9x9x3sHXCZKkJNBwjP7T9dR9X5MjGzxjXXXcMbb/gwlhnBNCJecK3xAfoAfAbtOkyO3IkTLfPGt53J4ge38a37MhRdjeNqmhPN2K6iVKzy9S9Xeeie3Xz3xtksXmSi6iN0WA4Lejs5b8UKvv7Fb3LrzTN59+veypyu+RhWDLd+AIzR4+rvpJ5GuwVL14DqQLxbF7GqG5ieWKaHebfsEqpGgZQE2BJjAhNcjOyN5h9cARalJfiwZ9KrNTOe513jeclGQ5ym/d5lmz3V7gcuB96iZJO/5zJj93mR/ciNdyHe4iYCT3AFQfr6XgJ6iB/j6kUM2+nAD5kuR1oedGjr6qYyOoIZCbF4xUq2PvboUS+fbgvzrve9lj3DT7Jv3QCYYK2WLdvp866nxFONGFKsZTooFmO63CRZiFwF1e2gvWU1fvJfFMmSqoJbB7UQ9A4kCDhT2hoKQf1JJODcBC0XQ/YeeHT9BH9U4o8Wl2A13Ugj8/96nu10soVPyvczY72iPrk6fG4v9Jrwmi5Et80N5wkjuz+0Id7EJsTTTXnX9p9FDjHCJSgPwtgQjGzNknnvn7LyE3tof+n7McLpQ5ris+FORKoTYerFKLY7Tqq3SLgnJslnRzuhBmoaXcmAMYzNFnY+/FN+85Ut3PYIzI+FGZnIMbcjyYrYCma0XEWnNZeB3Vv5+j1Pct/Bo9OKalWH3970KNp1OTgcYueeQebOn8HrrnsH6Xg3sqW6GN9SaTuGGqKYPUitUuKb//IZIraLO3sJV7xiDTsy69i4v4R2XVoMTcY1WHbGWURb5nP1ZUv4zpd/St4ZYlZXG4sSS/jqzzbxhfe+lO989N/4xve+xze+/T0+8idvZGp7CveshWze8VvmHkd/J2V0LS2VsKgjD34WgucdTgYIgboQMRKDeLmREKpBTxbWNByvAbvF24l1M3RulBXob71T7QD+EfgG4lAsR+xTBwIhK+B1Cr6lxRn7o5Y5CLQwjnizGoni15iGDSgjCkggRmwUMWRRpNEx7/tdTOPSEyPDANiVCtvWPX7Myy84ZzZDUwVu+/V3qBXFMjh7kJWKT4JQEO9KcNn5XdxX3kvBRjzYpYiByAEhqDxEsGtuBwJ/zPI+6yMoY5mGkAnOEHAG6L2ghxADUgNaINcD+qUEGym+EKIOe3+4x+uLTzr3o74pAmpZqeF4CzGMB5FiRGnI3AGXdMDKGVIbQfnRYYtgYkshUMX5oLcJDKNavOMUYIKugp4CZwSqA3KZhYthcKLE5s/9A6cPr6Ptzf+Eii+Fk+HnehKtWWRGQwxPZkmVh+mItNDcbXHE4/LiELqvRLU8QLW8lQ2bbuZH33yM3Ts1Fy9rZyKfY1FznJnNs6hlu3ArU2zd91vWD97F+tFJDOUV1zri3HoaKlt33+Osf2gjr333e3h83Xt5ySUX8IqrX0OlUiNsxbnpvz/HqnPO5Suf/wrNsQqP3jdC34jLa17XxSADDGayzGx1MFyTUq6EWXGpFCbo/f/aO9PouK4q3//uULOqJJUmW7asyfJsxyZOQkhIQgYykawAjRm6EyBNP3oxvAd0P1433Y8HzWt4K8zdDCsNIZABMnQnxHRISGxDBs+OHc+zJVmyxlJpqLnqDu/Dvreu5NgmtkI6H7TXqqVS3Vt1zz33nP/ZZ+//3rsxwhVX/jX33vsAew8ewbRtYv4XiBs1PP6LR5mY/17ePe+jpCq6OHT495w6sA778DFK6lkYPo5MC3QjBvg6kYHUhUy2PGVt3sbBiAqoDCJbp7nAOmACfK1w56uwKAZKVqoMDytQ1wZqMxCB6Ci8vwcKtozdMOKEq0Pwu935rAnRmhVgtgV/q8KnLK+6zltSmhATQz+ymrQ5nx1CwCxAmamgt4ARds4tIX3ci9A6VMQWmEDsxOVoK+usRLhQTOOz/+suOgfGWPfQMW/RS552ogV+f4BRo47s+An5zETsN5cgD0EXp4+yHKzJTsEqZDGYJZ8pgFqE9929iiOvJNlzrBsrB8ZBRKurAd9lYNqSR9g/HZ7un1osPJ6uGwegMbWkk4+ydksemSfdYhIdPQDVc+D224WFkB3CC96KIGaaEF5IeAi4GXgE7O2gRIA2JLnNuFRzHjoBsShEa8EXhHAAhlM2px5/iqi5H/+Hf4RSda2jal+45AYUJgZ0RobHGM12korMpiVSQ1VUQVUcnUvMp1gjBvmhJInjXWzZ+p9878ntVOR1FtXaJCZGqaixqa71MT5uE9JURieOcGK8n7XJLVTXBCCrMO5Scc4ipmFiWQXWPvwQxWwKJVfLE/9+J9fcfCXzG+fz3X99juVvO8rTz5wkky1hmTa1VUEefnIPLS0qTXNsXt5loWs+6itKjKYsel45SPfgIIcO/TeaiBBtvoJQRYx9fXvoGx/ggUNf4WT3Wt4WuJ1FDXMJLMyQL1nULJ2Fr/rcKdymBbrX2hDdi5fLeIDX1FXfBiwKQaXLE1OQAXUKlHq4QoGMHzYW4LcluKYKrs8iaLoalBFoOQVvN2Xc1SKRaFnET9eCjNHZyPEiMnY/rMJOSzTit6yZYRSZrJPBpRvREoeRhawDKICxDS/frI2AWg6hc7TiFVKtxttNnEMaFoXpS/ayacsL5FNniQzRgVkwHhplx6adWC1I55YQwJ9DOTG9XSkeehJOO6PIeHDTT5Yg0AbmLHh2236i9baXd7la7nnW9RCYD0oO+nWIVf3xLnxTxV3B3Ii1EJ4G7MOj/Lncx0rn/wLyPA+LzTV5BEoZqL5VHF+2Kkmdyvm9XeB1GRIO/1dZCvYKpKZdwPGhJIExqNRlhzgwAbkJqAxALCLMkZwFhX3H8D/7frjxHqi+G7EHXJjs2D9IcuQkGTNHjVXA7M+Tq4Zoq3MfinSWZVkU86dIZ3bxyqaHuP+xlyBjcPHbKrHNEaL1YCqQzuQIGAp+zcbSM9jFJBYFTiULrxugbMtiYmQYRVF48KePEays4A+be1GLFqPDWQ4f200h7zH5RyZKGIaNHbQYH7MZSUHL3DiD2X6iYZ2iYdLdM0piIM2qpjaaAvX8zafu4emn1nPv+h+SM3vZoR7keOEgq040siZwC4lMilnLFtPQejZVR+SCQbcK+HMFtMmakUskd8RAfD2Xg2hyVc6BQ5SjkUIqPDYMnwM+pMGVEcfjm0PA+Rj4LMGVf0DMn5/DyzUfQvCm0nmtQ1gS9QZ8LQh9Cjz5FkizcEYZxImbdv7G8LarIFrOAJ5d0AEnKp1zNWQV6kIWKR8ycU85x9OcMdJOUeG2D13Nib48u17sfO05fqRjlwKrwd5jYyVL8nxzeNnDXO77qNOmOPJcDyHOsh68LUgHGDUSBDd+rEg+55gYYpRrzw0+B/YrcPvqpQwn9xN5I6LK/hTiOtRON6O57B0Q26uNLDoDwHExoySPwNBhWPgZx5yQFOdk3oCwq9mGkV3hMLKl05zPNeBGMA6BslPMe0oOGAPVJ4nO9TgMDsNEAqobpPxPyA+ROJBLwaHPw/JXoOJbSMXY85fO3j+g2mkiVdXUz1tMS109sQoxG5VFAVVJYJdeZsOv7+PnT7xMRa5EdQjs0DjROoWJkk0pD76CRkDxEYkF8YUMlFQOX04ha9pTA57OIZoviKpqlAoZisUixeEkRjZGTX09hXxCAqImiWGIKjaRsKkMy+Ps6u2XjHE+g5yhoKsBMqUSr57q4eXCYZ754i5ymRK2XaI+FObWZUtoiRsUMikeO/kIluVn7b2vEpvfyIb3nb2tFwy6NwIr7XMb47OIEhxVkYF3EE+LA8kZ4INVJnxGgb9rgFACAZKXkLDRwzK43AjL2xFcKiHztRJRoOuAkCZZ5e4G7gIaDKQ09VtV4ggw9iEF4Zy4fQJIx7qANo4A4JBzjpPHUmkGe49z/Kjze3MR7biPKQmDply2yU9jY4DNXS8zmi562bQq8LK7zXX+H5bfMdztcwBhWjRIm9UUWAN4yWwcUTdC6HrIRJAFIw3GEKLJ94LW5LR3NqIhxmH+DdCzEYb6A+T3Q99hRbysbxU521hytVm3D91ZNYHYs4+A3Qsjh+HAC3Dp50FtwAui0KFkO99zGVZzkcXTcH7Xh9DEWsD3ISh+H6zdoBaQSRYCNQyhMWhqgqEEJAagdhZEqkGNgxJHPNjj96HQCdF1F9QNmcQJmjtW0rJ4JXVNdVS36KgNilgtnD5SbCikD/Pr793Djx7axynT5qqGEEuX1WLVJ5kwMmgECFohKpQGYlorQb0GzEHCo0WaTD+pROHM9twziFnKY572gHK5DD2dxwEbBR37THW2/BDvgNR+GMyJWSRbgiWXr2TocJCO+DI6lkTpTw7hO2mzPv0EBhY9uTT/9uLLXB7x8/73XU5dOEx/5wALYjGe3rz7nG29INBtQDix57ZcyIJ9px/8Ls2mj6lp8kowVA2hAny9FlTH8E4AeB4vhaEumsCXnWu7OzfXnxB23qPCFRY8YMOdSETiW9qmO4hXHLUbyauQQW5uHK8SxAQyAavxqluryANoQuy6ivPdJALg9cDveU09N0WH6++q43hXAn8sQ2CpDDLCCAC6ESYpp30jCPCqzt+Q81mrtFGdq2AZtlyn1zm+HOwhyO+H4M3grxcnT2a3cz+XORWkVWRMhKSdkZ5amtUg27btxCpBznydM+6/QtymuWHBbiikK2OI6adXgheGdsDW38HbPwyBlXhVbhwTQslkarBFBG9nE8eLjNNBuRj0a2D0V6DnIVoj23o1SDlfbuMcSZjTNwxtdcIRLpduV23Ir59q1joPqdBn0dq8iraOWQTnKygxxz/nYp5tY+WT7PzlT3nooX0M5yxuecdirls9n2BdgSP928nmTDTmEqCN6mATYbWOimiEkl1gsRanM9NPt66SKp3F9HVGmTpebMtEC4RpaF7I0IlDTv7c08QHc5ZCRwQefREMh3VVGYf3fOGzfOI9t9HcHmJ0W5J9D7zEdQdWM5T08dTxPXSxm1dzu9j/8MukrRKXBzVuXlhBT/TcUXLnDbqVwD0qLLcEE13GwOm3rcj9sCxKebucC4qjppxUzpJM+4YGSgZIgR2AMQOqJ5kt7Ep4LglbbdFilyIKhcvOibvXVCS88p9KolSfmSh1nuJqd5WIOh3D42NO17teQIzSzQiQbcbbulc71zCQGywiwLsU2UJUOk4UP9LpTri3cgTJqxpGJu741EsqGjRWhdi4dw+HcmNkA0hkyTFEW3UzFIWB+aCHJLzUwmnrPqAW2u+CzgEwArbHtKiUYxSQ8HoLVoxBxUoFvcbmpS2QXwhmxDl/P6LpO3b+V7UEVED0XVCsgsKWafbvn0omz213BzD52AgCuH1iUkhshY1rYdFqqLvJMacGKJsirCLkckyt3uvudE4g/e5+5gCyegOYG6RihS8PYSd4JlgNxSIke6CmWaJBi6ZT/LcKGVeuY+8CZWHLrTS31hHqEMCdCgA2VqHAnl/9mC9+7XHsYIAPvLOZuz9xKxpZ9nfvIByMAR3EgpdQGVhGVJuNjooeTGJqKQoBjeghG9M8H8A9s8xp7sBPEdN01a+pNiE9CFTCsvfC0V7YdlxBJcL+TV1c+648kbhJccKgclmMq77zHqKP7OY/vrEb1H7WLOqgqNSy68hWLGuEQ4ZJfMJmLHxuWD0v0J0DfFODP9PglyVI2PCFySeoMGZLAb4QyGCsoRxjvyUkUWvLJ51fXwn1gwjgKnByDuwZgNvccxQYj8F3kqK8NeJFcW9AUkvWuOfaQhSfW4IHgY8jJd3PW19SkBuY6/wfQyaIhgCgq6FMV+IIUFU516tCqhkGnGt2IROkymlTDtFkVflcSQldqDyZCmAfhUCHU+pnsg04CxhQUa+SLxiEOmwKlY4zK0+5IKSyEuxDEGhSYLFNaRtYfXh0sBVy70P9oNaAtVe2kpF2FaPGwjKhqACnIDgAOR+cTEsF6Xy/VIcwT+JpuEPSrnKZIh+kDouNMvpG9PGfUqZod8iYSCDjfQDsFIzsg42/gZo4dPw5AlKurdploZQgoDm4qk363ToEdDOTvuOAr9IGtTdDeq0siqYJmuMxrmiAogFj/VCzEPR2vHwzEbzoxguUFdfUE15xJsAFK13kwMM/4dv/9C2ypQq+8LE13HbnQgLqMANHOgkZNtWhNioDFzMreh21dUuojNRhWAZj2SN09u0ja6YIawZxHXLFC5i/jviCUepiEV7dtRVN0/DpQXL5qfH+egmMEcgvgsuugr3dfi6f/WUGho/RuaWX7fET1BX9+PJ5Sg06D3z/EXoSWfTgOFU9d7ArP4BiH6dIgpQBhWCIitK52RavS1friMFXG+D5OHxgLvwuCv9TgS7tND9CEO7VJSGNhROJVABKYIbhl4YELQAyCNoQbSeBDIhWUMfgssn9okOqQuy6n8cD2BISOjwlJYoB6bBg03zgUQR4z9td4NJ0VLyy8W56tHFkEth4KvaFSgABVje6y48koCjhRVlW4CXLdjUUG9gP9nrn+DKnTU5IfMFlQHTgeRoVOS92i8KO/Tl67TzFBF4tMCcyTK0HLoeWlbNRnlRRH4WmgE/Klx+HYLNC8N2Q2gbGC0g5JR20JoVCDxQToBRB3Q7Z3ZDrgMEem5HDjhJ9H6JVW077oJwCFdf23wP2MUi9BfLdnFFO5+26ZrFTCOAOyS4ksQu2PiKHV30E9Ll4JZecTGV2UVgIEXfrNll8iLab4LXRDAFQV4I/DloYsjrkbTFTqDpUzRaN2siBUoVUnZicxW4avKXIRYoEbExqj23bmEMluh98ike/+XXaalr58Zf/kQ9+/hYqZoGV70ex+tG0AJFAK3PrlrGofTUdHU3MnhekoaGCisgsqmo6qKqu5aJWlQbtNWSo1y+Kzoq3XU13ZxemaWKaFoXCa9X7YgqGjsKW51V+8WvIGUVCTT3csOAv+MOTm3lxy3YKChiHC6y751mSI138ofAIR3O7WG/+hpuXVjG/KY5Pg9Y5URZctJJY7RuQe+E9c+Gueikx/aVR+Mk4jFkChm5VXwAzBpuTEiU2X4Or60HZJZSYg3XwZK/g1EdWIHHjzyLk7qVwshl+uwtSydOKBWiCG/+MJM1xH8JW4LdIRGwzZZYKnWFJsP5FRCv+cQjWtMOaAzBxLs1p8oAuOD1TgRdPX3LeR3Cy6jD9nK2ubSZLOcBEmQX2RcikTOKZGYrI6pFBtFOXH+pEopFAopb6nN/yIVvceoRFEJP70QImI6M5JpSiVMPuQQC6BXxXAYekmtHh3X3QA9URlbvuupn/96u1aDZcdfNKXsjtQq0CawvQDVoj5PeZKBbYTWD3ASvBmgNHDwkAqTlJylPsRWzFNYijtAKxQefwFrsq572703iriqvdjlOmbjEGtgmD22DT/aBmYdVtEF0tXGaClKM1yyYqE4J+B4Bh6lichSzKBee7k3fHTaDPg3QWTEvCjPXZok37MlKqR3XAnQDlNKCcfo3zldO0ZNu2KfXmOPDDn7Hruft556VrWHXNjVRfGaM4tpXR0R1MJI4yPDbKWHYOqjKHisgiooFqfGEFohDM28zy1ZO3l5Aeaad21gHmVY9xPCfder5SN6uV4f6TjCQHnDaaZ9SYS0V4ZTtYOyykdKDC5r1PE6m7inpzGU8/9RuKJ/JUhxtpu/EKtj82RO34KkLxAkOZg/y4dyOfur6BAiFUPUd1tpPrFjSes22vC3R/cAB+dQjSlqfkAZy0wKpyQo0VmFAlQ9Aw0NciHFsssOfBU+MwaopJINsFERtYIYD8YA6+shm6R2W+3TF5YPmhNg034DnZDwP/AxnnGxFGgyuxINyvwSYTPqtKfbaHj0P2j21VbQREXfqiu/X34W33QQAt4vzteT29dw7pcq7bTDn7ne1oUf6VYCXBzILdhUzQQed7c+T6ygqxY1vjyMTvwYuIiiDg3CG/x0ngGOTrId2TgnfY+NolcTZjSL4HZ1cUCcKid6q88pzF/LYoLSuWww/WUlwK63fvxvKBPYKMnkWg+PxUrvSxaEGR7lSJUyfBnCcBDvZ+aZvPzcuQRTQul0scdPp3ttPuGNAE9uZp9u2fWlzQnMArnQLYfuj/DbzwC4hkYF4rNN3oBDME8GxjtvcdxSf/267Zyt1/KkhfBfAy9bufI5QzrQ0CCfDVgr4clMXIGBgD3xjCh3fb7NqfT9eaz1fyyFzxCeAWu3Ns/pcHOLzxOW6+6R7qOpZjVI0zcOJZcrmdjOdeYXg0ydiITm40S1WVH4sKLF1FCQEVYIcUArpOaKIRX0UzodoGYvXjrNI1Nveb5Es2iurDtlw6x9klWlXDZW+/kg2/e5z6uS2M9J3EtgzO5Jc1Ac0GxXYfik1Ei3JsaBu1wQVsGdzAc/yOL3/lc7z422fYO7CRCasH1daZ3xQlMWYxPgzz2sLctlohHr+K32zuP2f7XhfoloCBM4BW0oDCHNCdbdGuUeix4PYmeF8jMATJy2BvF8RHZaHdD+ybgMv2IslQbOHWdro/qoCmUd5q2ZUwkpG5Oo5Uevk6oiSBfDeN54iN+CUBzxYTtlmiaZzBZ/la8eHZIWYjWta4044YUK+g+KLYh1KQtGEvHghOR2Yh4dF5POdGEoojCBjuxKvyqiN2voAcs/eCvQyvZMdGxOlyifwGo4iWtBgB4bwUPIw32fTuFY2MFKJ1HkW0ThuUBug1LagElTRf/tt/xWyT8yzbQs2CFQOlQzzzSlWRsYkiA+MK1bUKA4qNeQJ8l0KhH3w9En01eMJpvxttt8i5pyQCBlUIKJ10+mE6jrTJE+yNpA262m0Gr6SUX15WCnp/ARsfE+dW6zxY9CFQa5G+dZ2bruZSohzNo6icmWqjIn026vydfC9hUK6GUDuSHKcOGcN+YDYoBWQX0euc785hF18utF8y8l0rYJPcOcGOJ54h35/gw3/1XaLtrWRKWYZ6t9E3up9i6Rg5Y4Cx8RKFIR+pbJ5iKUdtg4+iCXZOFiTFB0pIwQiECFfXYWlzWHZRJ/UnS+wbtsmXkHLvgK5rZZ6tgsLpFXzr5rSQGEuQzabJ5TKvyaw3WWykaorPDpA2xHbXM7abjD7O/Ph/5wrfFfQWN7DziW9z3/rnMZ1K2mPDkM9HCfh0/mXDfj54XZBjnSFuev8nmVd45ZzdNy3/e2cJusLAQikC+h85KQL60XfDST88PQ9uOwq39sPfIeBYAH6uSCQKtjz3duf3FBy7X2DSRerh5YJUjHgH8vf4pMP7gReDHh+3MgRNzvbH4o8ArkvDcWlSivN+Nl5ggQrUqkL9+XVGEvVuQbTI6Tp6fMgEyTi/1QQsQTqkBzgC2gJkAWhGVhY32qsGAdsXkK2Fjky4BMJxtkH5AAJuPYh2bENpQDK4qWkIdCLbhh6EslYC+iB9CAa3SwGA4y9rjGgpiThphNA8BPA3gfWSkPHzRTAGIFYTYV5jFVgQHvFBRkW7HJZ/3AnWmqDsLCvXgxtBdhVjeGGGY0hylz8SVfemi40XvGIifetwm0vH4MA/wpaH5XhTHDo+Avpix3nmOrAUPO6tijgvo+J8NF2u7+kLRjXSF4XTjumgzAflSvmLjlfF2B3XlcjCHmJqTuDpLEQ62MM2Bx86wf3/8A0MW+WGv/wc0WvaoNJgInOQ4exuxvKnSI6NMDycJTFcYLgvw6nuEfq7hunuHmU0bXnhoqrkYy75Qvgq6onWNrL40joqmyQ3hT4JqSaDqE/3oWs6iqKhKBq6rrNwfgt7tr/g3KZCLBDgXMktLXwsXbCMkK/MqyJpdLEp8zPamttoj8a58ZZV1IbCU76XLhVZ1NxIGB9dO9sIZW9h+6ZB7vn+D87ZfdMC3ZQN9x2E1Gp4PgKPIIm31/wMLv893PE8bErIeJnAMzX+yoYX4mDVgHUF5MKyTQ4CaRtOzkM0nxBkwhBNi+3Wz2txrgh8D0g3yvmaCf7XG+FYhXjNw05PzEaAL4xXCDIC7LYk23SXKZSpswQdnLcsRgb/Keevu/XsQ7zWVaAvQDpmDmIqSCOaYQhZKHLOZ25QRBMCBkeRAb0Cr2z7tZDIwfAWMI9C4QTicTQRwHNMAa4m1LAQLlm1iAVXhFA0COYUcuvBHKdc8NHeoVBf9GHvhX29aQ5tzVPohqJikT1mUzEPUmFInXKuFUKM7RGnTQXnvoPOawvSx3FkgblQUSa9piv2pJe75Y/KyzZg4nHY9tdw9PfCIpgThiUfEj6uEsZL2RnESwvpmBLsjPxe0YS826+nSwCPH30m0SlH9VHEy/HrajHuLJ+0g5yO2AWb9OYRXvr5j8jZY6xacwPhyyIouiVUALOXrNlDyugjWRwmnTdJZW1GUhZj42lO9fTQffQ4nQNZijlb+gBxAEaqIsTqW6hoaGLBqlVc/K45vPtdKq013vVN00JznmtD62KCgSA2FjYWiqazb+9u/LqNpqrMbaihaFmv0YYnS83sBZhaLaY5dbAUwgUar4xSGVjKfz6b5O9v+ioRzQPedL7Aut37SaMQNVdx8qWPcezrl/G/L37mnP03rdwLNvCjJGz6KRzJe1kH87a8ThcLGa9jwF8k4Y4AZAbgtwYENdGWTeBAEW5sBmrgqAHfNOGrwB3Az87Qjg15+McUfGk+6Etg4tg5Gu1qt5VIEu46vAqw4GliJWRiORpn2WH4RtKYYgi4mBC/Esa6wNqBgLAFnJREP+XQ25TT7l6njRE84rwP0XItykBsb0I4uAZiphhGTA9/QNJvznKOtSMc3TSyQhqgqjDxosa60l6UD9po10NVV4iRUJ5SwSo79YKFELE2HxPjExRO2ZyMF6h+ZwWjm9PQDeMFmKjAqxvo0th6EDBZLddWqsE+jgB/P16ymLeSTAZxBYzj0PNdOPBLqfZkAfEALLkdKm4DZZSy7bOcZayAN75wnGdRMPqgmOPMi4SCKAeOj2RKXStX3Gu44r53Ad6Np3UDlaahbiVfHGDzQw+RNUp88BOfZ3ZzFUoaKI1j0o0S7CNcXSRAjqyWwbIhn4IRC0ZLBlYhiXlyD5EDi6kNLmJBKEioQkFXoTKqE6xaimblqIjNIWfmCVUU0fxpHn3eYsdRscG59tm+riNYJZfSA6ZRIhyOUsqMkFLg1NCIYxJwb/x0Uaia1czff/QbPPDEt1i7/kFsdBTVz6JLrmbNp65jXdhPvFJjdraD6+ddz9rOtV5sjAXz6gIMK+sZyAzw9kv+hoV3XnvO/psW6II8z63nkdvAwvHx2PBveVCOy7wPBcANGHloED7yTqjPwoZXJThtC2dPXGMBP5iAp/ZC4Bgcn9weBRn4LZQThJeTrCScVzWihc1FJkYe0TQLzvGzaSDTFdeB1AsT68RxBnhbT9eWG0G0JCeclgBiFqjB00wNRAPOIYuFS9fbiMezO4aYKQLO/VrANlCyQj8qc9FD0DgIK68MsrtYoF8xqI5BZnaWUhDYD5H5CrkBG2NllqE86KqfyjqV+rjJ0R1FWcjagbVgR5z3KQTUBxCQcJ28PY7DrRMB5yCiqU/dzf3XyWn0MHsUxp6EQ/dC105hCKhIYM7Sq6Hm49KnqEzlxerIjsIJjLBtsApS08xA/A9nVMgUZIFOIM83coZ2udFsrnLgLhAaWFnhdCt5ZGy4HukL3AX8+4PfYWB0kJXv+0uqVrdjaQpKwQArgxoposVzRPQc1bEcxUHJdOcblyKddlbBtvKMW90Mjuyjc6SSYLKF9riCXgnVlQp2oAJFvwJbqaV1dZ7GrMXSi3tonpfgM/83z1Daa4tZmgo+tmkxkRxkYHB0aqPPNoEVnYaWlay+rp3lK7/Fnr1ddA93895b1/CV736B9tYGfB+7mW1PP0Zy/FluWPIOdg4foDd9gkikgtbWRfzVB++kvraORauXs3DxAgLhc2sL0wbd85UMQjvNIiAaQuy7IR/kHUfCrjR8aQ985GK4P+Oxcs4lFmKWnFKH3Y83cV3KVxGZ9J1MfQ5b8fKX+vAM0G9UIMQZRKkFuwc4BUYID1hNPNpazvlsFK/AXBTPudKLl1owjNyzjoTpphGNstc5vxbRdkeQCJWVwHqwdzjV2EPO6xT0VsBQdwZfu5RVSr8K2kJQD4EVgMyITWAVGGPgr4ZC0iByROOOP1vMt5MHBQRc4DSdNrsPcQ5iOjCZWi8t77Q94Zz3Rplx3gixJdgh+xIc+wkcWAdWxptAJRVWvB0aPy28WHqR/nY1XTefxmQHmiE5LfQglPJQGjrH9d2xnMYD3cmiOtfK4yXdUYVNsWEDJPfD7XdDIIYwBqZhdtlSSFB//Q1Yl1/MSI1GOAZhRUM1GlHMMFWVwxiJ4xQmwgxlfKjpIrWzIZ+Hgm5g+038po6/Kk8mZmJUiravRBE7taY4nrVZBAMthAIL6EsfIu/LYarSiT5AVWQnONmZpqPR39dXbmvQSSGgcxbLim1SmtBQgj7aL63lW//ne9xz/+Os+fgdLGltAEuhaX6c8WtuYsdLG8mpRZ769LMcGd3NoiVLaGltIhYTAvTrTVX8poOu60OIIWYGP2JAr6mANTXwbD+cLEFnBXxuu1DQLlhKeCS/sbOfBk6jsrypBSrtw3gljWoRUE3gPRUFD2xd8rxDByqHJXfhpRiMIBNzFGFXRPDSC4477/sQW/YRxNTQgQB8DskOVokErTRDsRGKYTknNFfYEpoFVi1UrQYlCVffFGXzMzbZ5izveVcb44ECRpspbU4i2uyAc/0iHp2tC4lGS+HZz7N4+SaqmVbU1BsmzrgwtsGJ+2DXs5Ae8fLSKAjOLV0G8z8N6nykH10Koo5HRSziVRU2wc6CpSBOJANSCWg/2wKvIM/N3XWdSZnSKUe4uZS2UgJ++Bg8twPuPgFf+2eoWuUcv8CdxMpPfpHVV7XTPNtHdUAhpOA4qlQU4vjsa4lXBzAm4hT1LSS1Y4xqKTL5AqrPxNIsIv4I0dlR4rNqqIkraFXIwlS+XwWoRFNqwIqRSVTzu80pRibEvKBq4BAYqKyIM55OYmNTOg1aS4pKSLXIn61f1SD5tM3YsMHceT7e+8mVXHrjAoJBFQVIJ0YJ6VUsvaiF9qXz8PksAgGdVUr7WX7wj4tyLjrFjMzIjMzIjLyxMt2ULTMyIzMyIzNyHjIDujMyIzMyI2+izIDujMzIjMzImygzoDsjMzIjM/ImygzozsiMzMiMvIkyA7ozMiMzMiNvovx/r/uVI7tFLaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = next(train_ds.create_dict_iterator())\n",
    "images = data[\"image\"]\n",
    "labels = data[\"label\"]\n",
    "print(\"Tensor of image\", images.shape)\n",
    "print(\"Labels:\",labels)\n",
    "class_name = {0:\"ants\",1:\"bees\"}\n",
    "count = 1\n",
    "# 输出测试图\n",
    "for i in images:\n",
    "    plt.subplot(1,4,count)\n",
    "    plt.imshow(i.asnumpy().transpose(1,2,0))\n",
    "    plt.title(class_name[int(labels[count-1].asnumpy())])\n",
    "    plt.xticks([])\n",
    "    count += 1\n",
    "    plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义网络相关的计算模块\n",
    "通过定义计算模块，为网络提供权重变化和学习增益的更新。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size):\n",
    "    \"\"\"方差缩放初始化设定\"\"\"\n",
    "    fan_in = in_channel * kernel_size * kernel_size\n",
    "    scale = 1.0\n",
    "    scale /= max(1., fan_in)\n",
    "    stddev = (scale ** 0.5) / .87962566103423978\n",
    "    mu, sigma = 0, stddev\n",
    "    weight = truncnorm(-2, 2, loc=mu, scale=sigma).rvs(out_channel * in_channel * kernel_size * kernel_size)\n",
    "    weight = np.reshape(weight, (out_channel, in_channel, kernel_size, kernel_size))\n",
    "    return Tensor(weight, dtype=mstype.float32)\n",
    "\n",
    "\n",
    "def _weight_variable(shape, factor=0.01):\n",
    "    \"\"\"权重变化\"\"\"\n",
    "    init_value = np.random.randn(*shape).astype(np.float32) * factor\n",
    "    return Tensor(init_value)\n",
    "\n",
    "def calculate_gain(nonlinearity, param=None):\n",
    "    \"\"\"计算增益\"\"\"\n",
    "    linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']\n",
    "    res = 0\n",
    "    # 判断线性函数方法\n",
    "    if nonlinearity in linear_fns or nonlinearity == 'sigmoid':\n",
    "        res = 1\n",
    "    elif nonlinearity == 'tanh':\n",
    "        res = 5.0 / 3\n",
    "    elif nonlinearity == 'relu':\n",
    "        res = math.sqrt(2.0)\n",
    "    elif nonlinearity == 'leaky_relu':\n",
    "        if param is None:\n",
    "            negative_slope = 0.01\n",
    "        elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):\n",
    "            negative_slope = param\n",
    "        else:\n",
    "            raise ValueError(\"negative_slope {} not a valid number\".format(param))\n",
    "        res = math.sqrt(2.0 / (1 + negative_slope ** 2))\n",
    "    else:\n",
    "        raise ValueError(\"Unsupported nonlinearity {}\".format(nonlinearity))\n",
    "    return res\n",
    "\n",
    "def _calculate_fan_in_and_fan_out(tensor):\n",
    "    \"\"\"计算扇入和扇出\"\"\"\n",
    "    dimensions = len(tensor)\n",
    "    # 判断张量长度\n",
    "    if dimensions < 2:\n",
    "        raise ValueError(\"Fan in and fan out can not be computed for tensor with fewer than 2 dimensions\")\n",
    "    if dimensions == 2:  # 线性\n",
    "        fan_in = tensor[1]\n",
    "        fan_out = tensor[0]\n",
    "    else:\n",
    "        num_input_fmaps = tensor[1]\n",
    "        num_output_fmaps = tensor[0]\n",
    "        receptive_field_size = 1\n",
    "        if dimensions > 2:\n",
    "            receptive_field_size = tensor[2] * tensor[3]\n",
    "        fan_in = num_input_fmaps * receptive_field_size\n",
    "        fan_out = num_output_fmaps * receptive_field_size\n",
    "    return fan_in, fan_out\n",
    "\n",
    "def _calculate_correct_fan(tensor, mode):\n",
    "    \"\"\"更正函数计算\"\"\"\n",
    "    mode = mode.lower()\n",
    "    valid_modes = ['fan_in', 'fan_out']\n",
    "    if mode not in valid_modes:\n",
    "        raise ValueError(\"Mode {} not supported, please use one of {}\".format(mode, valid_modes))\n",
    "    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)\n",
    "    return fan_in if mode == 'fan_in' else fan_out\n",
    "\n",
    "\n",
    "def kaiming_normal(inputs_shape, a=0, mode='fan_in', nonlinearity='leaky_relu'):\n",
    "    fan = _calculate_correct_fan(inputs_shape, mode)\n",
    "    gain = calculate_gain(nonlinearity, a)\n",
    "    std = gain / math.sqrt(fan)\n",
    "    return np.random.normal(0, std, size=inputs_shape).astype(np.float32)\n",
    "\n",
    "def kaiming_uniform(inputs_shape, a=0., mode='fan_in', nonlinearity='leaky_relu'):\n",
    "    fan = _calculate_correct_fan(inputs_shape, mode)\n",
    "    gain = calculate_gain(nonlinearity, a)\n",
    "    std = gain / math.sqrt(fan)\n",
    "    bound = math.sqrt(3.0) * std \n",
    "    return np.random.uniform(-bound, bound, size=inputs_shape).astype(np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义卷积模块\n",
    "通过定义网络所需要的卷积块，满足网络的卷积需求。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _conv3x3(in_channel, out_channel, stride=1, use_se=False, res_base=False):\n",
    "    \"\"\"定义3*3卷积块\"\"\"\n",
    "    if use_se:\n",
    "        weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=3)\n",
    "    else:\n",
    "        weight_shape = (out_channel, in_channel, 3, 3)\n",
    "        weight = Tensor(kaiming_normal(weight_shape, mode=\"fan_out\", nonlinearity='relu'))\n",
    "    if res_base:\n",
    "        return nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride,\n",
    "                         padding=1, pad_mode='pad', weight_init=weight)\n",
    "    return nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride,\n",
    "                     padding=0, pad_mode='same', weight_init=weight)\n",
    "\n",
    "\n",
    "def _conv1x1(in_channel, out_channel, stride=1, use_se=False, res_base=False):\n",
    "    \"\"\"定义1*1卷积块\"\"\"\n",
    "    if use_se:\n",
    "        weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=1)\n",
    "    else:\n",
    "        weight_shape = (out_channel, in_channel, 1, 1)\n",
    "        weight = Tensor(kaiming_normal(weight_shape, mode=\"fan_out\", nonlinearity='relu'))\n",
    "    if res_base:\n",
    "        return nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=stride,\n",
    "                         padding=0, pad_mode='pad', weight_init=weight)\n",
    "    return nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=stride,\n",
    "                     padding=0, pad_mode='same', weight_init=weight)\n",
    "\n",
    "\n",
    "\n",
    "def _conv7x7(in_channel, out_channel, stride=1, use_se=False, res_base=False):\n",
    "    \"\"\"定义7*7卷积块\"\"\"\n",
    "    if use_se:\n",
    "        weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=7)\n",
    "    else:\n",
    "        weight_shape = (out_channel, in_channel, 7, 7)\n",
    "        weight = Tensor(kaiming_normal(weight_shape, mode=\"fan_out\", nonlinearity='relu'))\n",
    "    if res_base:\n",
    "        return nn.Conv2d(in_channel, out_channel,\n",
    "                         kernel_size=7, stride=stride, padding=3, pad_mode='pad', weight_init=weight)\n",
    "    return nn.Conv2d(in_channel, out_channel,\n",
    "                     kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义网络层\n",
    "为了构造ResNet网络，需要定义组成该网络所需的网络层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _bn(channel, res_base=False):\n",
    "    \"\"\"定义bn层\"\"\"\n",
    "    if res_base:\n",
    "        return nn.BatchNorm2d(channel, eps=1e-5, momentum=0.1,\n",
    "                              gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)\n",
    "    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,\n",
    "                          gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)\n",
    "\n",
    "def _bn_last(channel):\n",
    "    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,\n",
    "                          gamma_init=0, beta_init=0, moving_mean_init=0, moving_var_init=1)\n",
    "\n",
    "\n",
    "def _fc(in_channel, out_channel, use_se=False):\n",
    "    \"\"\"定义fc层\"\"\"\n",
    "    if use_se:\n",
    "        weight = np.random.normal(loc=0, scale=0.01, size=out_channel * in_channel)\n",
    "        weight = Tensor(np.reshape(weight, (out_channel, in_channel)), dtype=mstype.float32)\n",
    "    else:\n",
    "        weight_shape = (out_channel, in_channel)\n",
    "        weight = Tensor(kaiming_uniform(weight_shape, a=math.sqrt(5)))\n",
    "    return nn.Dense(in_channel, out_channel, has_bias=True, weight_init=weight, bias_init=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义残差函数\n",
    "为了防止网络退化问题，我们来定义残差函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Cell):\n",
    "    \"\"\"\n",
    "    定义ResNet V1 残差函数\n",
    "    \"\"\"\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(self,\n",
    "                 in_channel,\n",
    "                 out_channel,\n",
    "                 stride=1,\n",
    "                 use_se=False, se_block=False):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        self.stride = stride\n",
    "        self.use_se = use_se\n",
    "        self.se_block = se_block\n",
    "        channel = out_channel // self.expansion\n",
    "        self.conv1 = _conv1x1(in_channel, channel, stride=1, use_se=self.use_se)\n",
    "        self.bn1 = _bn(channel)\n",
    "        if self.use_se and self.stride != 1:\n",
    "            self.e2 = nn.SequentialCell([_conv3x3(channel, channel, stride=1, use_se=True), _bn(channel),\n",
    "                                         nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same')])\n",
    "        else:\n",
    "            self.conv2 = _conv3x3(channel, channel, stride=stride, use_se=self.use_se)\n",
    "            self.bn2 = _bn(channel)\n",
    "\n",
    "        self.conv3 = _conv1x1(channel, out_channel, stride=1, use_se=self.use_se)\n",
    "        self.bn3 = _bn_last(out_channel)\n",
    "        if self.se_block:\n",
    "            self.se_global_pool = ops.operations.ReduceMean(keep_dims=False)\n",
    "            self.se_dense_0 = _fc(out_channel, int(out_channel / 4), use_se=self.use_se)\n",
    "            self.se_dense_1 = _fc(int(out_channel / 4), out_channel, use_se=self.use_se)\n",
    "            self.se_sigmoid = nn.Sigmoid()\n",
    "            self.se_mul = ops.operations.Mul()\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "        self.down_sample = False\n",
    "\n",
    "        if stride != 1 or in_channel != out_channel:\n",
    "            self.down_sample = True\n",
    "        self.down_sample_layer = None\n",
    "\n",
    "        if self.down_sample:\n",
    "            if self.use_se:\n",
    "                if stride == 1:\n",
    "                    self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel,\n",
    "                                                                         stride, use_se=self.use_se), _bn(out_channel)])\n",
    "                else:\n",
    "                    self.down_sample_layer = nn.SequentialCell([nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same'),\n",
    "                                                                _conv1x1(in_channel, out_channel, 1,\n",
    "                                                                         use_se=self.use_se), _bn(out_channel)])\n",
    "            else:\n",
    "                self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride,\n",
    "                                                                     use_se=self.use_se), _bn(out_channel)])\n",
    "\n",
    "    def construct(self, x):\n",
    "        \"\"\"\n",
    "        定义ResNet V1残差函数\n",
    "        \"\"\"\n",
    "        identity = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "        if self.use_se and self.stride != 1:\n",
    "            out = self.e2(out)\n",
    "        else:\n",
    "            out = self.conv2(out)\n",
    "            out = self.bn2(out)\n",
    "            out = self.relu(out)\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "        if self.se_block:\n",
    "            out_se = out\n",
    "            out = self.se_global_pool(out, (2, 3))\n",
    "            out = self.se_dense_0(out)\n",
    "            out = self.relu(out)\n",
    "            out = self.se_dense_1(out)\n",
    "            out = self.se_sigmoid(out)\n",
    "            out = ops.functional.reshape(out, ops.functional.shape(out) + (1, 1))\n",
    "            out = self.se_mul(out, out_se)\n",
    "\n",
    "        if self.down_sample:\n",
    "            identity = self.down_sample_layer(identity)\n",
    "\n",
    "        out = out + identity\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义ResNet网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(nn.Cell):\n",
    "    \"\"\"\n",
    "    定义ResNet\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 block,\n",
    "                 layer_nums,\n",
    "                 in_channels,\n",
    "                 out_channels,\n",
    "                 strides,\n",
    "                 num_classes,\n",
    "                 use_se=False,\n",
    "                 res_base=False):\n",
    "        super(ResNet, self).__init__()\n",
    "\n",
    "        if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:\n",
    "            raise ValueError(\"the length of layer_num, in_channels, out_channels list must be 4!\")\n",
    "        self.use_se = use_se\n",
    "        self.res_base = res_base\n",
    "        self.se_block = False\n",
    "        if self.use_se:\n",
    "            self.se_block = True\n",
    "\n",
    "        if self.use_se:\n",
    "            self.conv1_0 = _conv3x3(3, 32, stride=2, use_se=self.use_se)\n",
    "            self.bn1_0 = _bn(32)\n",
    "            self.conv1_1 = _conv3x3(32, 32, stride=1, use_se=self.use_se)\n",
    "            self.bn1_1 = _bn(32)\n",
    "            self.conv1_2 = _conv3x3(32, 64, stride=1, use_se=self.use_se)\n",
    "        else:\n",
    "            self.conv1 = _conv7x7(3, 64, stride=2, res_base=self.res_base)\n",
    "        self.bn1 = _bn(64, self.res_base)\n",
    "        self.relu = ops.operations.ReLU()\n",
    "\n",
    "        if self.res_base:\n",
    "            self.pad = nn.Pad(paddings=((0, 0), (0, 0), (1, 1), (1, 1)))\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode=\"valid\")\n",
    "        else:\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode=\"same\")\n",
    "\n",
    "        self.layer1 = self._make_layer(block,\n",
    "                                       layer_nums[0],\n",
    "                                       in_channel=in_channels[0],\n",
    "                                       out_channel=out_channels[0],\n",
    "                                       stride=strides[0],\n",
    "                                       use_se=self.use_se)\n",
    "        self.layer2 = self._make_layer(block,\n",
    "                                       layer_nums[1],\n",
    "                                       in_channel=in_channels[1],\n",
    "                                       out_channel=out_channels[1],\n",
    "                                       stride=strides[1],\n",
    "                                       use_se=self.use_se)\n",
    "        self.layer3 = self._make_layer(block,\n",
    "                                       layer_nums[2],\n",
    "                                       in_channel=in_channels[2],\n",
    "                                       out_channel=out_channels[2],\n",
    "                                       stride=strides[2],\n",
    "                                       use_se=self.use_se,\n",
    "                                       se_block=self.se_block)\n",
    "        self.layer4 = self._make_layer(block,\n",
    "                                       layer_nums[3],\n",
    "                                       in_channel=in_channels[3],\n",
    "                                       out_channel=out_channels[3],\n",
    "                                       stride=strides[3],\n",
    "                                       use_se=self.use_se,\n",
    "                                       se_block=self.se_block)\n",
    "\n",
    "        self.mean = ops.operations.ReduceMean(keep_dims=True)\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.end_point = _fc(out_channels[3], num_classes, use_se=self.use_se)\n",
    "\n",
    "    def _make_layer(self, block, layer_num, in_channel, out_channel, stride, use_se=False, se_block=False):\n",
    "        \"\"\"\n",
    "        构造ResNet网络结构\n",
    "        \"\"\"\n",
    "        layers = []\n",
    "\n",
    "        resnet_block = block(in_channel, out_channel, stride=stride, use_se=use_se)\n",
    "        layers.append(resnet_block)\n",
    "        if se_block:\n",
    "            for _ in range(1, layer_num - 1):\n",
    "                resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se)\n",
    "                layers.append(resnet_block)\n",
    "            resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se, se_block=se_block)\n",
    "            layers.append(resnet_block)\n",
    "        else:\n",
    "            for _ in range(1, layer_num):\n",
    "                resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se)\n",
    "                layers.append(resnet_block)\n",
    "        return nn.SequentialCell(layers)\n",
    "\n",
    "    def construct(self, x):\n",
    "        if self.use_se:\n",
    "            x = self.conv1_0(x)\n",
    "            x = self.bn1_0(x)\n",
    "            x = self.relu(x)\n",
    "            x = self.conv1_1(x)\n",
    "            x = self.bn1_1(x)\n",
    "            x = self.relu(x)\n",
    "            x = self.conv1_2(x)\n",
    "        else:\n",
    "            x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        if self.res_base:\n",
    "            x = self.pad(x)\n",
    "        c1 = self.maxpool(x)\n",
    "\n",
    "        c2 = self.layer1(c1)\n",
    "        c3 = self.layer2(c2)\n",
    "        c4 = self.layer3(c3)\n",
    "        c5 = self.layer4(c4)\n",
    "\n",
    "        out = self.mean(c5, (2, 3))\n",
    "        out = self.flatten(out)\n",
    "        out = self.end_point(out)\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义ResNet50网络\n",
    "通过前面ResNet网络构造ResNet50网络。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resnet50(class_num=10):\n",
    "    \"\"\"\n",
    "    创建ResNet50神经网络\n",
    "    \"\"\"\n",
    "    return ResNet(ResidualBlock,\n",
    "              [3, 4, 6, 3],\n",
    "              [64, 256, 512, 1024],\n",
    "              [256, 512, 1024, 2048],\n",
    "              [1, 2, 2, 2],\n",
    "              class_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型\n",
    "现在，让我们自定义一个数据收集的回调类`EvalCallBack`，用于实现下面两种信息：\n",
    "- 训练过程中，每一个epoch结束之后，训练集的损失值和验证集的模型精度。\n",
    "- 保存精度最高的模型。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindspore import save_checkpoint\n",
    "from mindspore.train.callback import TimeMonitor, Callback, LossMonitor\n",
    "\n",
    "def apply_eval(eval_param):\n",
    "    eval_model = eval_param['model']\n",
    "    eval_ds = eval_param['dataset']\n",
    "    metrics_name = eval_param['metrics_name']\n",
    "    res = eval_model.eval(eval_ds)\n",
    "    return res[metrics_name]\n",
    "\n",
    "class EvalCallBack(Callback):\n",
    "    \"\"\"\n",
    "    回调训练评价类\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, eval_function, eval_param_dict, interval=1, eval_start_epoch=1, save_best_ckpt=True,\n",
    "                 ckpt_directory=\"./\", besk_ckpt_name=\"best.ckpt\", metrics_name=\"acc\", num_epochs=20):\n",
    "        super(EvalCallBack, self).__init__()\n",
    "        self.eval_param_dict = eval_param_dict\n",
    "        self.eval_function = eval_function\n",
    "        self.eval_start_epoch = eval_start_epoch\n",
    "        if interval < 1:\n",
    "            raise ValueError(\"interval should >= 1.\")\n",
    "        self.interval = interval\n",
    "        self.save_best_ckpt = save_best_ckpt\n",
    "        self.best_res = 0\n",
    "        self.best_epoch = 0\n",
    "        if not os.path.isdir(ckpt_directory):\n",
    "            os.makedirs(ckpt_directory)\n",
    "        self.best_ckpt_path = os.path.join(ckpt_directory, besk_ckpt_name)\n",
    "        self.metrics_name = metrics_name\n",
    "\n",
    "    def remove_ckpoint_file(self, file_name):\n",
    "        \"\"\"每轮训练后只保留精度最好的ckpt文件\"\"\"\n",
    "        os.chmod(file_name, stat.S_IWRITE)\n",
    "        os.remove(file_name)\n",
    "    def epoch_end(self, run_context):\n",
    "        \"\"\"训练反馈\"\"\"\n",
    "        cb_params = run_context.original_args()\n",
    "        cur_epoch = cb_params.cur_epoch_num\n",
    "        loss_epoch =  cb_params.net_outputs\n",
    "        if cur_epoch >= self.eval_start_epoch and (cur_epoch - self.eval_start_epoch) % self.interval == 0:\n",
    "            res = self.eval_function(self.eval_param_dict)\n",
    "            print('Epoch {}/{}'.format(cur_epoch, num_epochs))\n",
    "            print('-' * 10)\n",
    "            print('train Loss: {}'.format(loss_epoch))\n",
    "            print('val Acc: {}'.format(res))\n",
    "            if res >= self.best_res:\n",
    "                self.best_res = res\n",
    "                self.best_epoch = cur_epoch\n",
    "                if self.save_best_ckpt:\n",
    "                    if os.path.exists(self.best_ckpt_path):\n",
    "                        self.remove_ckpoint_file(self.best_ckpt_path)\n",
    "                    save_checkpoint(cb_params.train_network, self.best_ckpt_path)\n",
    "    def end(self, run_context):\n",
    "        print(\"End training, the best {0} is: {1}, the best {0} epoch is {2}\".format(self.metrics_name,\n",
    "                                                                                     self.best_res,\n",
    "                                                                                     self.best_epoch), flush=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化模型预测\n",
    "\n",
    "通过下面的函数，展示一些预测图像，并删除项目中需要重置的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindspore import load_checkpoint, load_param_into_net\n",
    "\n",
    "def visualize_model(best_ckpt_path,val_ds):\n",
    "    \"\"\"实例化网络\"\"\"\n",
    "    net = resnet50(2)\n",
    "    param_dict = load_checkpoint(best_ckpt_path)\n",
    "    load_param_into_net(net,param_dict)\n",
    "    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True,reduction='mean')\n",
    "    model = Model(net, loss,metrics={\"Accuracy\":Accuracy()})\n",
    "    data = next(val_ds.create_dict_iterator())\n",
    "    images = data[\"image\"].asnumpy()\n",
    "    labels = data[\"label\"].asnumpy()\n",
    "    class_name = {0:\"ants\",1:\"bees\"}\n",
    "    output = model.predict(Tensor(data['image']))\n",
    "    pred = np.argmax(output.asnumpy(),axis=1)\n",
    "    err_num = []\n",
    "    index = 1\n",
    "    # 展示预测图像\n",
    "    for i in range(len(labels)):\n",
    "        plt.subplot(2,2,i+1)\n",
    "        color = 'blue' if pred[i] == labels[i] else 'red'\n",
    "        plt.title('pre:{}'.format(class_name[pred[i]]), color=color)\n",
    "        plt.imshow(images[i].transpose(1,2,0))\n",
    "        plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_checkpoint_parameter_by_list(origin_dict, param_filter):\n",
    "    \"\"\"从训练好的ckpt文件里删除需要重置的参数\"\"\"\n",
    "    for key in list(origin_dict.keys()):\n",
    "        for name in param_filter:\n",
    "            if name in key:\n",
    "                print(\"Delete parameter from checkpoint: \", key)\n",
    "                del origin_dict[key]\n",
    "                break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型微调\n",
    "\n",
    "加载预训练的模型并重置最终的全连接层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete parameter from checkpoint:  end_point.weight\n",
      "Delete parameter from checkpoint:  end_point.bias\n",
      "Delete parameter from checkpoint:  moments.end_point.weight\n",
      "Delete parameter from checkpoint:  moments.end_point.bias\n"
     ]
    }
   ],
   "source": [
    "net = resnet50(2)\n",
    "num_epochs=20\n",
    "param_dict = load_checkpoint('resnet50.ckpt')\n",
    "filter_list = [x.name for x in net.end_point.get_parameters()]\n",
    "filter_checkpoint_parameter_by_list(param_dict, filter_list)\n",
    "load_param_into_net(net,param_dict)\n",
    "opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.9)\n",
    "loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True,reduction='mean')\n",
    "model = Model(net, loss,opt,metrics={\"Accuracy\":Accuracy()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练和评估\n",
    "\n",
    "运行下面代码，开始模型训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "----------\n",
      "train Loss: 0.9686852\n",
      "val Acc: 0.7105263157894737\n",
      "epoch time: 12580.445 ms, per step time: 206.237 ms\n",
      "Epoch 2/20\n",
      "----------\n",
      "train Loss: 0.44091043\n",
      "val Acc: 0.6118421052631579\n",
      "epoch time: 5018.593 ms, per step time: 82.272 ms\n",
      "Epoch 3/20\n",
      "----------\n",
      "train Loss: 0.910527\n",
      "val Acc: 0.7105263157894737\n",
      "epoch time: 5521.028 ms, per step time: 90.509 ms\n",
      "Epoch 4/20\n",
      "----------\n",
      "train Loss: 0.5526486\n",
      "val Acc: 0.7236842105263158\n",
      "epoch time: 5453.967 ms, per step time: 89.409 ms\n",
      "Epoch 5/20\n",
      "----------\n",
      "train Loss: 0.5758235\n",
      "val Acc: 0.7236842105263158\n",
      "epoch time: 5440.765 ms, per step time: 89.193 ms\n",
      "Epoch 6/20\n",
      "----------\n",
      "train Loss: 0.426515\n",
      "val Acc: 0.6842105263157895\n",
      "epoch time: 5027.957 ms, per step time: 82.426 ms\n",
      "Epoch 7/20\n",
      "----------\n",
      "train Loss: 0.33777326\n",
      "val Acc: 0.7631578947368421\n",
      "epoch time: 5399.855 ms, per step time: 88.522 ms\n",
      "Epoch 8/20\n",
      "----------\n",
      "train Loss: 0.9343228\n",
      "val Acc: 0.756578947368421\n",
      "epoch time: 5115.569 ms, per step time: 83.862 ms\n",
      "Epoch 9/20\n",
      "----------\n",
      "train Loss: 0.9867699\n",
      "val Acc: 0.7171052631578947\n",
      "epoch time: 5013.284 ms, per step time: 82.185 ms\n",
      "Epoch 10/20\n",
      "----------\n",
      "train Loss: 0.122110516\n",
      "val Acc: 0.7763157894736842\n",
      "epoch time: 5580.847 ms, per step time: 91.489 ms\n",
      "Epoch 11/20\n",
      "----------\n",
      "train Loss: 0.35179645\n",
      "val Acc: 0.7763157894736842\n",
      "epoch time: 5527.845 ms, per step time: 90.620 ms\n",
      "Epoch 12/20\n",
      "----------\n",
      "train Loss: 0.56950986\n",
      "val Acc: 0.8157894736842105\n",
      "epoch time: 5484.829 ms, per step time: 89.915 ms\n",
      "Epoch 13/20\n",
      "----------\n",
      "train Loss: 0.36948645\n",
      "val Acc: 0.8223684210526315\n",
      "epoch time: 5475.081 ms, per step time: 89.755 ms\n",
      "Epoch 14/20\n",
      "----------\n",
      "train Loss: 0.30380312\n",
      "val Acc: 0.7368421052631579\n",
      "epoch time: 5120.890 ms, per step time: 83.949 ms\n",
      "Epoch 15/20\n",
      "----------\n",
      "train Loss: 0.43103072\n",
      "val Acc: 0.8026315789473685\n",
      "epoch time: 5040.272 ms, per step time: 82.627 ms\n",
      "Epoch 16/20\n",
      "----------\n",
      "train Loss: 0.25074488\n",
      "val Acc: 0.8157894736842105\n",
      "epoch time: 4950.397 ms, per step time: 81.154 ms\n",
      "Epoch 17/20\n",
      "----------\n",
      "train Loss: 0.47597006\n",
      "val Acc: 0.7894736842105263\n",
      "epoch time: 4986.107 ms, per step time: 81.739 ms\n",
      "Epoch 18/20\n",
      "----------\n",
      "train Loss: 0.32440752\n",
      "val Acc: 0.8092105263157895\n",
      "epoch time: 4999.177 ms, per step time: 81.954 ms\n",
      "Epoch 19/20\n",
      "----------\n",
      "train Loss: 0.49091864\n",
      "val Acc: 0.7960526315789473\n",
      "epoch time: 4937.662 ms, per step time: 80.945 ms\n",
      "Epoch 20/20\n",
      "----------\n",
      "train Loss: 0.509585\n",
      "val Acc: 0.8092105263157895\n",
      "epoch time: 4976.568 ms, per step time: 81.583 ms\n",
      "End training, the best acc is: 0.8223684210526315, the best acc epoch is 13\n"
     ]
    }
   ],
   "source": [
    "train_ds =  create_dataset(train_data_path)\n",
    "val_ds = create_dataset(val_data_path)\n",
    "eval_param_dict = {\"model\":model,\"dataset\":val_ds,\"metrics_name\":\"Accuracy\"}\n",
    "eval_cb = EvalCallBack(apply_eval, eval_param_dict,)\n",
    "model.train(num_epochs,train_ds, callbacks=[eval_cb, TimeMonitor()], dataset_sink_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model('best.ckpt',val_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 固定特征进行训练 \n",
    "\n",
    "在这里，我们需要冻结除最后一层之外的所有网络。 设置`requires_grad == False`冻结参数，以便不在反向传播中计算梯度。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete parameter from checkpoint:  end_point.weight\n",
      "Delete parameter from checkpoint:  end_point.bias\n",
      "Delete parameter from checkpoint:  moments.end_point.weight\n",
      "Delete parameter from checkpoint:  moments.end_point.bias\n"
     ]
    }
   ],
   "source": [
    "net = resnet50(2)\n",
    "num_epochs=20\n",
    "param_dict = load_checkpoint('resnet50_ascend_v111.ckpt')\n",
    "filter_list = [x.name for x in net.end_point.get_parameters()]\n",
    "filter_checkpoint_parameter_by_list(param_dict, filter_list)\n",
    "load_param_into_net(net,param_dict)\n",
    "for param in net.get_parameters():\n",
    "    if param.name not in [\"end_point.weight\",\"end_point.bias\"]:\n",
    "         param.requires_grad = False\n",
    "opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)\n",
    "loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True,reduction='mean')\n",
    "model = Model(net, loss,opt,metrics={\"Accuracy\":Accuracy()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练和评估\n",
    "\n",
    "与没有预训练模型相比，将节约一大半时间，因为此时可以不用计算部分梯度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "----------\n",
      "train Loss: 18.609592\n",
      "val Acc: 0.6381578947368421\n",
      "epoch time: 7167.215 ms, per step time: 117.495 ms\n",
      "Epoch 2/20\n",
      "----------\n",
      "train Loss: 11.478684\n",
      "val Acc: 0.5592105263157895\n",
      "epoch time: 2681.014 ms, per step time: 43.951 ms\n",
      "Epoch 3/20\n",
      "----------\n",
      "train Loss: 0.2935766\n",
      "val Acc: 0.5328947368421053\n",
      "epoch time: 2586.290 ms, per step time: 42.398 ms\n",
      "Epoch 4/20\n",
      "----------\n",
      "train Loss: 7.7069426\n",
      "val Acc: 0.5723684210526315\n",
      "epoch time: 2573.789 ms, per step time: 42.193 ms\n",
      "Epoch 5/20\n",
      "----------\n",
      "train Loss: 5.1382387e-05\n",
      "val Acc: 0.6644736842105263\n",
      "epoch time: 2740.495 ms, per step time: 44.926 ms\n",
      "Epoch 6/20\n",
      "----------\n",
      "train Loss: 16.870192\n",
      "val Acc: 0.5789473684210527\n",
      "epoch time: 2570.333 ms, per step time: 42.137 ms\n",
      "Epoch 7/20\n",
      "----------\n",
      "train Loss: 8.476288\n",
      "val Acc: 0.6118421052631579\n",
      "epoch time: 2616.749 ms, per step time: 42.898 ms\n",
      "Epoch 8/20\n",
      "----------\n",
      "train Loss: 14.265462\n",
      "val Acc: 0.6776315789473685\n",
      "epoch time: 2886.543 ms, per step time: 47.320 ms\n",
      "Epoch 9/20\n",
      "----------\n",
      "train Loss: 4.859915\n",
      "val Acc: 0.6973684210526315\n",
      "epoch time: 2787.328 ms, per step time: 45.694 ms\n",
      "Epoch 10/20\n",
      "----------\n",
      "train Loss: 7.657242\n",
      "val Acc: 0.6118421052631579\n",
      "epoch time: 2626.861 ms, per step time: 43.063 ms\n",
      "Epoch 11/20\n",
      "----------\n",
      "train Loss: 10.21109\n",
      "val Acc: 0.631578947368421\n",
      "epoch time: 2684.526 ms, per step time: 44.009 ms\n",
      "Epoch 12/20\n",
      "----------\n",
      "train Loss: 8.151873\n",
      "val Acc: 0.6052631578947368\n",
      "epoch time: 2605.486 ms, per step time: 42.713 ms\n",
      "Epoch 13/20\n",
      "----------\n",
      "train Loss: 25.159967\n",
      "val Acc: 0.6710526315789473\n",
      "epoch time: 2628.136 ms, per step time: 43.084 ms\n",
      "Epoch 14/20\n",
      "----------\n",
      "train Loss: 1.2489088\n",
      "val Acc: 0.6776315789473685\n",
      "epoch time: 2644.188 ms, per step time: 43.347 ms\n",
      "Epoch 15/20\n",
      "----------\n",
      "train Loss: 6.883577\n",
      "val Acc: 0.5263157894736842\n",
      "epoch time: 2648.278 ms, per step time: 43.414 ms\n",
      "Epoch 16/20\n",
      "----------\n",
      "train Loss: 11.7939625\n",
      "val Acc: 0.6513157894736842\n",
      "epoch time: 2622.153 ms, per step time: 42.986 ms\n",
      "Epoch 17/20\n",
      "----------\n",
      "train Loss: 0.008153722\n",
      "val Acc: 0.6447368421052632\n",
      "epoch time: 2662.436 ms, per step time: 43.646 ms\n",
      "Epoch 18/20\n",
      "----------\n",
      "train Loss: 6.115326\n",
      "val Acc: 0.5592105263157895\n",
      "epoch time: 2637.179 ms, per step time: 43.232 ms\n",
      "Epoch 19/20\n",
      "----------\n",
      "train Loss: 1.0587248\n",
      "val Acc: 0.7105263157894737\n",
      "epoch time: 2785.071 ms, per step time: 45.657 ms\n",
      "Epoch 20/20\n",
      "----------\n",
      "train Loss: 18.615145\n",
      "val Acc: 0.625\n",
      "epoch time: 2563.007 ms, per step time: 42.017 ms\n",
      "End training, the best acc is: 0.7105263157894737, the best acc epoch is 19\n"
     ]
    }
   ],
   "source": [
    "train_ds =  create_dataset(train_data_path)\n",
    "val_ds = create_dataset(val_data_path)\n",
    "eval_param_dict = {\"model\":model,\"dataset\":val_ds,\"metrics_name\":\"Accuracy\"}\n",
    "eval_cb = EvalCallBack(apply_eval, eval_param_dict,)\n",
    "model.train(num_epochs,train_ds, callbacks=[eval_cb, TimeMonitor()], dataset_sink_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model('best.ckpt',val_ds)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-wc37] *",
   "language": "python",
   "name": "conda-env-.conda-wc37-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
